THE GAME & EMBODIED RESEARCH INDEX

Game & Embodied
Agents

Research on agents that perceive, plan, and act.
From virtual worlds to the physical world.

115 curated resourcesMaintained by Renmin ChengSnapshot · 14 Sep 2026
Browse the collection

Five years of leading venues One year of selected arXiv research

Curated order. Paper years refer to the venue; source dates retain their original meaning.

115 unique resources

Category counts overlap: dataset and benchmark papers also appear in Papers. All resources counts each record once.

Getting startedRead, build, evaluate, watch

All resources

Download catalogue

Research articles6Explore research articles

GAME2022OpenAI

Learning Minecraft from video

Learning to play Minecraft with Video PreTraining

Explains how inverse-dynamics labels turn large video collections into training data for Minecraft behavior.

Research perspectives
Details & evidence

Credit: OpenAI

Environment & tasks: Virtual environments.

Observations & actions: See the original article for the system interface and examples.

Scope & limitations: Native keyboard and mouse control; separates pretraining from task fine-tuning.

Why included: Official author or research-team article providing context for the collection.

Evidence status: Official source checked; not independently reproduced.

Checked 2026-09-14

Added 2026-09-14

GAME2024Google DeepMind

SIMA: A generalist game agent

A generalist AI agent for 3D virtual environments

Introduces a language-instructed agent trained across different virtual environments.

Research perspectives
Details & evidence

Credit: Google DeepMind

Environment & tasks: Virtual environments.

Observations & actions: See the original article for the system interface and examples.

Scope & limitations: Screen observations and keyboard/mouse actions; a research overview rather than a new benchmark record.

Why included: Official author or research-team article providing context for the collection.

Evidence status: Official source checked; not independently reproduced.

Checked 2026-09-14

Added 2026-09-14

GAME2025Google DeepMind · SIMA Team

SIMA 2: Playing, reasoning, and learning

SIMA 2: An Agent that Plays, Reasons, and Learns With You in Virtual 3D Worlds

Describes Gemini-based reasoning, interaction, and generalization in virtual worlds.

Research perspectives
Details & evidence

Credit: Google DeepMind · SIMA Team

Environment & tasks: Virtual environments.

Observations & actions: See the original article for the system interface and examples.

Scope & limitations: Claims and examples concern virtual environments; they do not establish physical robot competence.

Why included: Official author or research-team article providing context for the collection.

Evidence status: Official source checked; not independently reproduced.

Checked 2026-09-14

Added 2026-09-14

EMBODIED2024Physical Intelligence

π0: Our first generalist policy

π0: Our First Generalist Policy

Introduces a vision-language-action policy with flow-matching action outputs and examples of adaptation to robot tasks.

Research perspectives
Details & evidence

Credit: Physical Intelligence

Environment & tasks: Robot learning and physical interaction.

Observations & actions: See the original article for the system interface and examples.

Scope & limitations: Some demanding tasks use post-training; selected demonstrations are not general success guarantees.

Why included: Official author or research-team article providing context for the collection.

Evidence status: Official source checked; not independently reproduced.

Checked 2026-09-14

Added 2026-09-14 · Revised 2026-09-15

EMBODIED2026Hugging Face · LeRobot team

LeRobot: Imagine, evaluate, improve

LeRobot v0.6.0: Imagine, Evaluate, Improve

Documents a robot-learning release connecting policy training, evaluation, reward models, and corrective deployment data.

Research perspectives
Details & evidence

Credit: Hugging Face · LeRobot team

Environment & tasks: Robot learning and physical interaction.

Observations & actions: See the original article for the system interface and examples.

Scope & limitations: Release-specific features require compatible hardware, dependencies, and model configuration.

Why included: Official author or research-team article providing context for the collection.

Evidence status: Official source checked; not independently reproduced.

Checked 2026-09-14

Added 2026-09-14

EMBODIED2026Phillip Isola · MIT

Robot-use agents

Explores agents that reason over goals and operate robots through sensor and actuator interfaces.

Research perspectives
Details & evidence

Credit: Phillip Isola · MIT

Environment & tasks: Robot learning and physical interaction.

Observations & actions: See the original article for the system interface and examples.

Scope & limitations: A perspective on robot interfaces and agent loops, not a peer-reviewed evaluation.

Why included: Official author or research-team article providing context for the collection.

Evidence status: Official source checked; not independently reproduced.

Checked 2026-09-14

Added 2026-09-14

Papers83Explore papers

First page of Mastering diverse control tasks through world models
GAME + EMBODIEDNature 2025

Mastering diverse control tasks through world models (DreamerV3)

Trains policies inside a learned world model with a shared algorithm configuration across diverse control tasks, connecting game and robot learning.

World modelsReinforcement learning
Details & sources

Authors: Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy Lillicrap

Project name: DreamerV3

Bibliographic source: Publisher ↗

Publisher record date: 2025-04-02

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-g05,
  title = {Mastering diverse control tasks through world models},
  author = {Danijar Hafner and Jurgis Pasukonis and Jimmy Ba and Timothy Lillicrap},
  year = {2025},
  url = {https://www.nature.com/articles/s41586-025-08744-2},
  doi = {10.1038/s41586-025-08744-2}
}

Sources checked 2026-09-14 · Knowledge-base ID G05

Added 2026-09-14

First page of OpenVLA: An Open-Source Vision-Language-Action Model
EMBODIEDCoRL 2024

OpenVLA: An Open-Source Vision-Language-Action Model

Unifies vision, language, and robot actions in an open model, with released weights and training tools for adaptation to new tasks.

Vision-language-actionRobot learning
Details & sources

Authors: Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan P Foster, Pannag R Sanketi, Quan Vuong, Thomas Kollar, Benjamin Burchfiel, Russ Tedrake, Dorsa Sadigh, Sergey Levine, Percy Liang, Chelsea Finn

Project name: OpenVLA

Bibliographic source: Publisher ↗

Publisher record date: 2025-01-12

Publication note: The conference took place in 2024; PMLR proceedings were published in 2025.

BibTeX citation
@misc{openvla,
  title = {OpenVLA: An Open-Source Vision-Language-Action Model},
  author = {Moo Jin Kim and Karl Pertsch and Siddharth Karamcheti and Ted Xiao and Ashwin Balakrishna and Suraj Nair and Rafael Rafailov and Ethan P Foster and Pannag R Sanketi and Quan Vuong and Thomas Kollar and Benjamin Burchfiel and Russ Tedrake and Dorsa Sadigh and Sergey Levine and Percy Liang and Chelsea Finn},
  year = {2025},
  url = {https://proceedings.mlr.press/v270/kim25c.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of π0: A Vision-Language-Action Flow Model for General Robot Control
EMBODIEDRSS 2025

π0: A Vision-Language-Action Flow Model for General Robot Control

Introduces a generalist VLA that augments a pretrained vision-language model with a flow-matching action expert for dexterous, high-frequency robot control.

Vision-language-actionFlow matching
Details & sources

Authors: Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Lucy Xiaoyang Shi, Laura Smith, James Tanner, Quan Vuong, Anna Walling, Haohuan Wang, Ury Zhilinsky

Project name: π0

Bibliographic source: arXiv ↗

First arXiv submission: 2024-10-31

BibTeX citation
@inproceedings{pi0-paper,
  title = {π0: A Vision-Language-Action Flow Model for General Robot Control},
  author = {Kevin Black and Noah Brown and Danny Driess and Adnan Esmail and Michael Equi and Chelsea Finn and Niccolo Fusai and Lachy Groom and Karol Hausman and Brian Ichter and Szymon Jakubczak and Tim Jones and Liyiming Ke and Sergey Levine and Adrian Li-Bell and Mohith Mothukuri and Suraj Nair and Karl Pertsch and Lucy Xiaoyang Shi and Laura Smith and James Tanner and Quan Vuong and Anna Walling and Haohuan Wang and Ury Zhilinsky},
  year = {2025},
  booktitle = {RSS},
  url = {https://arxiv.org/abs/2410.24164}
}

Sources checked 2026-09-15

Added 2026-09-15

First page of Voyager: An Open-Ended Embodied Agent with Large Language Models
GAMETMLR 2024

Voyager: An Open-Ended Embodied Agent with Large Language Models

Combines an automatic curriculum, reusable code skills, and execution feedback for continuing exploration and skill acquisition in Minecraft.

Programs & toolsExploration & skills
Details & sources

Authors: Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, Anima Anandkumar

Project name: Voyager

Bibliographic source: arXiv ↗

First arXiv submission: 2023-05-25

BibTeX citation
@misc{voyager,
  title = {Voyager: An Open-Ended Embodied Agent with Large Language Models},
  author = {Guanzhi Wang and Yuqi Xie and Yunfan Jiang and Ajay Mandlekar and Chaowei Xiao and Yuke Zhu and Linxi Fan and Anima Anandkumar},
  year = {2023},
  url = {https://openreview.net/forum?id=P8E4Br72j3}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of FAST: Efficient Action Tokenization for Vision-Language-Action Models
EMBODIEDRSS 2025

FAST: Efficient Action Tokenization for Vision-Language-Action Models (π0-FAST)

Compresses continuous action chunks with a discrete cosine transform and byte-pair encoding, enabling efficient autoregressive VLA training and the π0-FAST policy.

Vision-language-actionAction tokenization
Details & sources

Authors: Karl Pertsch, Kyle Stachowicz, Brian Ichter, Danny Driess, Suraj Nair, Quan Vuong, Oier Mees, Chelsea Finn, Sergey Levine

Project name: π0-FAST

Bibliographic source: arXiv ↗

First arXiv submission: 2025-01-16

BibTeX citation
@inproceedings{pi0-fast,
  title = {FAST: Efficient Action Tokenization for Vision-Language-Action Models},
  author = {Karl Pertsch and Kyle Stachowicz and Brian Ichter and Danny Driess and Suraj Nair and Quan Vuong and Oier Mees and Chelsea Finn and Sergey Levine},
  year = {2025},
  booktitle = {RSS},
  url = {https://arxiv.org/abs/2501.09747}
}

Sources checked 2026-09-15

Added 2026-09-15

First page of Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
EMBODIEDRSS 2023

Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

Models robot action sequences as conditional denoising, generating multimodal continuous actions from visual observations.

Diffusion modelsImitation learning
Details & sources

Authors: Cheng Chi, Zhenjia Xu, Siyuan Feng, Eric Cousineau, Yilun Du, Benjamin Burchfiel, Russ Tedrake, Shuran Song

Project name: Diffusion Policy

Bibliographic source: arXiv ↗

First arXiv submission: 2023-03-07

BibTeX citation
@misc{diffusion-policy,
  title = {Diffusion Policy: Visuomotor Policy Learning via Action Diffusion},
  author = {Cheng Chi and Zhenjia Xu and Siyuan Feng and Eric Cousineau and Yilun Du and Benjamin Burchfiel and Russ Tedrake and Shuran Song},
  year = {2023},
  url = {https://diffusion-policy.cs.columbia.edu/}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of π0.5: a Vision-Language-Action Model with Open-World Generalization
EMBODIEDCoRL 2025

π0.5: a Vision-Language-Action Model with Open-World Generalization

Extends π0 with heterogeneous co-training across robot actions, semantic subtasks, object grounding, language instructions, and web data for open-world generalization.

Vision-language-actionOpen-world generalization
Details & sources

Authors: Physical Intelligence, Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Manuel Y. Galliker, Dibya Ghosh, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Devin LeBlanc, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Allen Z. Ren, Lucy Xiaoyang Shi, Laura Smith, Jost Tobias Springenberg, Kyle Stachowicz, James Tanner, Quan Vuong, Homer Walke, Anna Walling, Haohuan Wang, Lili Yu, Ury Zhilinsky

Project name: π0.5

Bibliographic source: arXiv ↗

First arXiv submission: 2025-04-22

BibTeX citation
@inproceedings{pi05,
  title = {π0.5: a Vision-Language-Action Model with Open-World Generalization},
  author = {Physical Intelligence and Kevin Black and Noah Brown and James Darpinian and Karan Dhabalia and Danny Driess and Adnan Esmail and Michael Equi and Chelsea Finn and Niccolo Fusai and Manuel Y. Galliker and Dibya Ghosh and Lachy Groom and Karol Hausman and Brian Ichter and Szymon Jakubczak and Tim Jones and Liyiming Ke and Devin LeBlanc and Sergey Levine and Adrian Li-Bell and Mohith Mothukuri and Suraj Nair and Karl Pertsch and Allen Z. Ren and Lucy Xiaoyang Shi and Laura Smith and Jost Tobias Springenberg and Kyle Stachowicz and James Tanner and Quan Vuong and Homer Walke and Anna Walling and Haohuan Wang and Lili Yu and Ury Zhilinsky},
  year = {2025},
  booktitle = {CoRL},
  url = {https://arxiv.org/abs/2504.16054}
}

Sources checked 2026-09-15

Added 2026-09-15

First page of Imitating Human Behaviour with Diffusion Models
GAMEICLR 2023

Imitating Human Behaviour with Diffusion Models

Uses conditional diffusion to jointly predict mouse movement and firing, capturing multiple plausible choices in human behavior.

Imitation learningDiffusion models
Details & sources

Authors: Tim Pearce, Tabish Rashid, Anssi Kanervisto, Dave Bignell, Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan, Ida Momennejad, Katja Hofmann, Sam Devlin

Project name: Diffusion for Human Behaviour

Bibliographic source: arXiv ↗

First arXiv submission: 2023-01-25

Original knowledge-base review label: Spot checks of key claims

BibTeX citation
@misc{kb-f02,
  title = {Imitating Human Behaviour with Diffusion Models},
  author = {Tim Pearce and Tabish Rashid and Anssi Kanervisto and Dave Bignell and Mingfei Sun and Raluca Georgescu and Sergio Valcarcel Macua and Shan Zheng Tan and Ida Momennejad and Katja Hofmann and Sam Devlin},
  year = {2023},
  url = {https://arxiv.org/abs/2301.10677}
}

Sources checked 2026-09-14 · Knowledge-base ID F02

Added 2026-09-14

First page of Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better
EMBODIEDNeurIPS 2025

Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better (π0.5 + KI)

Separates continuous-action gradients from the pretrained VLM backbone while using FAST tokens and vision-language co-training to preserve semantic knowledge and speed up learning.

Vision-language-actionKnowledge insulation
Details & sources

Authors: Danny Driess, Jost Tobias Springenberg, Brian Ichter, Lili Yu, Adrian Li-Bell, Karl Pertsch, Allen Z. Ren, Homer Walke, Quan Vuong, Lucy Xiaoyang Shi, Sergey Levine

Project name: π0.5 + KI

Bibliographic source: arXiv ↗

First arXiv submission: 2025-05-29

BibTeX citation
@inproceedings{pi05-ki,
  title = {Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better},
  author = {Danny Driess and Jost Tobias Springenberg and Brian Ichter and Lili Yu and Adrian Li-Bell and Karl Pertsch and Allen Z. Ren and Homer Walke and Quan Vuong and Lucy Xiaoyang Shi and Sergey Levine},
  year = {2025},
  booktitle = {NeurIPS},
  url = {https://arxiv.org/abs/2505.23705}
}

Sources checked 2026-09-15

Added 2026-09-15

First page of SIMA 2: A Generalist Embodied Agent for Virtual Worlds
GAMEarXiv · 2025.12Selected preprint

SIMA 2: A Generalist Embodied Agent for Virtual Worlds

Connects Gemini reasoning and interaction to multiple 3D games, studying instruction following, cross-environment generalization, and self-improvement.

Language & actionExploration & skills
Details & sources

Authors: SIMA team, Adrian Bolton, Alexander Lerchner, Alexandra Cordell, Alexandre Moufarek, Andrew Bolt, Andrew Lampinen, Anna Mitenkova, Arne Olav Hallingstad, Bojan Vujatovic, Bonnie Li, Cong Lu, Daan Wierstra, Daniel P. Sawyer, Daniel Slater, David Reichert, Davide Vercelli, Demis Hassabis, Drew A. Hudson, Duncan Williams, Ed Hirst, Fabio Pardo, Felix Hill, Frederic Besse, Hannah Openshaw, Harris Chan, Hubert Soyer, Jane X. Wang, Jeff Clune, John Agapiou, John Reid, Joseph Marino, Junkyung Kim, Karol Gregor, Kaustubh Sridhar, Kay McKinney, Laura Kampis, Lei M. Zhang, Loic Matthey, Luyu Wang, Maria Abi Raad, Maria Loks-Thompson, Martin Engelcke, Matija Kecman, Matthew Jackson, Maxime Gazeau, Ollie Purkiss, Oscar Knagg, Peter Stys, Piermaria Mendolicchio, Raia Hadsell, Rosemary Ke, Ryan Faulkner, Sarah Chakera, Satinder Singh Baveja, Shane Legg, Sheleem Kashem, Tayfun Terzi, Thomas Keck, Tim Harley, Tim Scholtes, Tyson Roberts, Volodymyr Mnih, Yulan Liu, Zhengdong Wang, Zoubin Ghahramani

Project name: SIMA 2

Bibliographic source: arXiv ↗

First arXiv submission: 2025-12-04

Evidence for editorial selection: Official research report ↗

BibTeX citation
@misc{sima2,
  title = {SIMA 2: A Generalist Embodied Agent for Virtual Worlds},
  author = {{SIMA team} and Adrian Bolton and Alexander Lerchner and Alexandra Cordell and Alexandre Moufarek and Andrew Bolt and Andrew Lampinen and Anna Mitenkova and Arne Olav Hallingstad and Bojan Vujatovic and Bonnie Li and Cong Lu and Daan Wierstra and Daniel P. Sawyer and Daniel Slater and David Reichert and Davide Vercelli and Demis Hassabis and Drew A. Hudson and Duncan Williams and Ed Hirst and Fabio Pardo and Felix Hill and Frederic Besse and Hannah Openshaw and Harris Chan and Hubert Soyer and Jane X. Wang and Jeff Clune and John Agapiou and John Reid and Joseph Marino and Junkyung Kim and Karol Gregor and Kaustubh Sridhar and Kay McKinney and Laura Kampis and Lei M. Zhang and Loic Matthey and Luyu Wang and Maria Abi Raad and Maria Loks-Thompson and Martin Engelcke and Matija Kecman and Matthew Jackson and Maxime Gazeau and Ollie Purkiss and Oscar Knagg and Peter Stys and Piermaria Mendolicchio and Raia Hadsell and Rosemary Ke and Ryan Faulkner and Sarah Chakera and Satinder Singh Baveja and Shane Legg and Sheleem Kashem and Tayfun Terzi and Thomas Keck and Tim Harley and Tim Scholtes and Tyson Roberts and Volodymyr Mnih and Yulan Liu and Zhengdong Wang and Zoubin Ghahramani},
  year = {2025},
  url = {https://arxiv.org/abs/2512.04797}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Emergence of Human to Robot Transfer in Vision-Language-Action Models
EMBODIEDRSS 2026

Emergence of Human to Robot Transfer in Vision-Language-Action Models (π0.5 + ego)

Shows that human-to-robot transfer emerges with sufficiently diverse VLA pretraining, then adapts π0.5 with egocentric human demonstrations through simple co-training.

Vision-language-actionHuman-to-robot transfer
Details & sources

Authors: Simar Kareer, Karl Pertsch, James Darpinian, Judy Hoffman, Danfei Xu, Sergey Levine, Chelsea Finn, Suraj Nair

Project name: π0.5 + ego

Bibliographic source: arXiv ↗

First arXiv submission: 2025-12-27

BibTeX citation
@inproceedings{pi05-ego,
  title = {Emergence of Human to Robot Transfer in Vision-Language-Action Models},
  author = {Simar Kareer and Karl Pertsch and James Darpinian and Judy Hoffman and Danfei Xu and Sergey Levine and Chelsea Finn and Suraj Nair},
  year = {2026},
  booktitle = {RSS},
  url = {https://arxiv.org/abs/2512.22414}
}

Sources checked 2026-09-15

Added 2026-09-15

First page of F.A.C.U.L.: Language-Based Interaction with AI Companions in Gaming
GAMEAAAI 2026

F.A.C.U.L.: Language-Based Interaction with AI Companions in Gaming

Turns player language into action programs for game companions through spatial grounding, intent routing, and behavior-tree execution.

Language & actionMulti-agent learning
Details & sources

Authors: Wenya Wei, Sipeng Yang, Qixian Zhou, Ruochen Liu, Xuelei Zhang, Yifu Yuan, Yan Jiang, Yongle Luo, Hailong Wang, Tianzhou Wang, Peipei Jin, Wangtong Liu, Zhou Zhao, Xiaogang Jin, Elvis S. Liu

Project name: F.A.C.U.L.

Bibliographic source: OpenAlex ↗

Indexed publication date (OpenAlex): 2026-03-14

Original knowledge-base review label: Spot checks of key claims

BibTeX citation
@misc{kb-f07,
  title = {F.A.C.U.L.: Language-Based Interaction with AI Companions in Gaming},
  author = {Wenya Wei and Sipeng Yang and Qixian Zhou and Ruochen Liu and Xuelei Zhang and Yifu Yuan and Yan Jiang and Yongle Luo and Hailong Wang and Tianzhou Wang and Peipei Jin and Wangtong Liu and Zhou Zhao and Xiaogang Jin and Elvis S. Liu},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/38842}
}

Sources checked 2026-09-14 · Knowledge-base ID F07

Added 2026-09-14

First page of π0.7: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
EMBODIEDarXiv · 2026.04Selected preprint

π0.7: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities

Uses diverse multimodal context—language, subgoal images, control labels, speed, and quality—to steer a single generalist policy and recombine skills across robots and tasks.

Vision-language-actionCompositional generalization
Details & sources

Authors: Physical Intelligence, Bo Ai, Ali Amin, Raichelle Aniceto, Ashwin Balakrishna, Greg Balke, Kevin Black, George Bokinsky, Shihao Cao, Thomas Charbonnier, Vedant Choudhary, Foster Collins, Ken Conley, Grace Connors, James Darpinian, Karan Dhabalia, Maitrayee Dhaka, Jared DiCarlo, Danny Driess, Michael Equi, Adnan Esmail, Yunhao Fang, Chelsea Finn, Catherine Glossop, Thomas Godden, Ivan Goryachev, Lachlan Groom, Haroun Habeeb, Hunter Hancock, Karol Hausman, Gashon Hussein, Victor Hwang, Brian Ichter, Connor Jacobsen, Szymon Jakubczak, Rowan Jen, Tim Jones, Gregg Kammerer, Ben Katz, Liyiming Ke, Mairbek Khadikov, Chandra Kuchi, Marinda Lamb, Devin LeBlanc, Brendon LeCount, Sergey Levine, Xinyu Li, Adrian Li-Bell, Vladislav Lialin, Zhonglin Liang, Wallace Lim, Yao Lu, Enyu Luo, Vishnu Mano, Nandan Marwaha, Aikys Mongush, Liam Murphy, Suraj Nair, Tyler Patterson, Karl Pertsch, Allen Z. Ren, Gavin Schelske, Charvi Sharma, Baifeng Shi, Lucy Xiaoyang Shi, Laura Smith, Jost Tobias Springenberg, Kyle Stachowicz, Will Stoeckle, Jiaming Tang, Jimmy Tanner, Shalom Tekeste, Marcel Torne, Kyle Vedder, Quan Vuong, Anna Walling, Haohuan Wang, Jason Wang, XuDong Wang, Chris Whalen, Samuel Whitmore, Blake Williams, Charles Xu, Sukwon Yoo, Lili Yu, Wuming Zhang, Zhuoyang Zhang, Ury Zhilinsky

Project name: π0.7

Bibliographic source: arXiv ↗

First arXiv submission: 2026-04-16

Evidence for editorial selection: Official research report ↗

BibTeX citation
@misc{pi07,
  title = {π0.7: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities},
  author = {Physical Intelligence and Bo Ai and Ali Amin and Raichelle Aniceto and Ashwin Balakrishna and Greg Balke and Kevin Black and George Bokinsky and Shihao Cao and Thomas Charbonnier and Vedant Choudhary and Foster Collins and Ken Conley and Grace Connors and James Darpinian and Karan Dhabalia and Maitrayee Dhaka and Jared DiCarlo and Danny Driess and Michael Equi and Adnan Esmail and Yunhao Fang and Chelsea Finn and Catherine Glossop and Thomas Godden and Ivan Goryachev and Lachlan Groom and Haroun Habeeb and Hunter Hancock and Karol Hausman and Gashon Hussein and Victor Hwang and Brian Ichter and Connor Jacobsen and Szymon Jakubczak and Rowan Jen and Tim Jones and Gregg Kammerer and Ben Katz and Liyiming Ke and Mairbek Khadikov and Chandra Kuchi and Marinda Lamb and Devin LeBlanc and Brendon LeCount and Sergey Levine and Xinyu Li and Adrian Li-Bell and Vladislav Lialin and Zhonglin Liang and Wallace Lim and Yao Lu and Enyu Luo and Vishnu Mano and Nandan Marwaha and Aikys Mongush and Liam Murphy and Suraj Nair and Tyler Patterson and Karl Pertsch and Allen Z. Ren and Gavin Schelske and Charvi Sharma and Baifeng Shi and Lucy Xiaoyang Shi and Laura Smith and Jost Tobias Springenberg and Kyle Stachowicz and Will Stoeckle and Jiaming Tang and Jimmy Tanner and Shalom Tekeste and Marcel Torne and Kyle Vedder and Quan Vuong and Anna Walling and Haohuan Wang and Jason Wang and XuDong Wang and Chris Whalen and Samuel Whitmore and Blake Williams and Charles Xu and Sukwon Yoo and Lili Yu and Wuming Zhang and Zhuoyang Zhang and Ury Zhilinsky},
  year = {2026},
  url = {https://arxiv.org/abs/2604.15483}
}

Sources checked 2026-09-15

Added 2026-09-15

First page of Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models
EMBODIEDICML 2025

Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models

Uses a high-level vision-language model to interpret complex instructions and feedback, while a low-level vision-language-action model executes subtasks.

Vision-language-actionPlanning & memory
Details & sources

Authors: Lucy Xiaoyang Shi, Brian Ichter, Michael Robert Equi, Liyiming Ke, Karl Pertsch, Quan Vuong, James Tanner, Anna Walling, Haohuan Wang, Niccolo Fusai, Adrian Li-Bell, Danny Driess, Lachy Groom, Sergey Levine, Chelsea Finn

Project name: Hi Robot

Bibliographic source: Publisher ↗

Publisher record date: 2025-10-06

BibTeX citation
@misc{hirobot,
  title = {Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models},
  author = {Lucy Xiaoyang Shi and Brian Ichter and Michael Robert Equi and Liyiming Ke and Karl Pertsch and Quan Vuong and James Tanner and Anna Walling and Haohuan Wang and Niccolo Fusai and Adrian Li-Bell and Danny Driess and Lachy Groom and Sergey Levine and Chelsea Finn},
  year = {2025},
  url = {https://proceedings.mlr.press/v267/shi25d.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Diffusion Models Are Real-Time Game Engines
GAMEICLR 2025

Diffusion Models Are Real-Time Game Engines (GameNGen)

Generates interactive DOOM frames conditioned on past images and player actions, exploring neural networks as game simulators.

World modelsEnvironment generation
Details & sources

Authors: Dani Valevski, Yaniv Leviathan, Moab Arar, Shlomi Fruchter

Project name: GameNGen

Bibliographic source: Publisher ↗

Publisher record date: 2025-05-01

Original knowledge-base review label: Spot checks of key claims

BibTeX citation
@misc{kb-w01,
  title = {Diffusion Models Are Real-Time Game Engines},
  author = {Dani Valevski and Yaniv Leviathan and Moab Arar and Shlomi Fruchter},
  year = {2025},
  url = {https://proceedings.iclr.cc/paper_files/paper/2025/hash/b71ecea210f7159f31e46631fe5c838f-Abstract-Conference.html}
}

Sources checked 2026-09-14 · Knowledge-base ID W01

Added 2026-09-14

First page of π*0.6: a VLA That Learns From Experience
EMBODIEDarXiv · 2025.11Selected preprint

π*0.6: a VLA That Learns From Experience (π*0.6 / RECAP)

Combines demonstrations, autonomous execution, and human corrections to improve vision-language-action models through advantage-conditioned reinforcement learning.

Vision-language-actionReinforcement learning
Details & sources

Authors: Physical Intelligence, Ali Amin, Raichelle Aniceto, Ashwin Balakrishna, Kevin Black, Ken Conley, Grace Connors, James Darpinian, Karan Dhabalia, Jared DiCarlo, Danny Driess, Michael Equi, Adnan Esmail, Yunhao Fang, Chelsea Finn, Catherine Glossop, Thomas Godden, Ivan Goryachev, Lachy Groom, Hunter Hancock, Karol Hausman, Gashon Hussein, Brian Ichter, Szymon Jakubczak, Rowan Jen, Tim Jones, Ben Katz, Liyiming Ke, Chandra Kuchi, Marinda Lamb, Devin LeBlanc, Sergey Levine, Adrian Li-Bell, Yao Lu, Vishnu Mano, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Allen Z. Ren, Charvi Sharma, Lucy Xiaoyang Shi, Laura Smith, Jost Tobias Springenberg, Kyle Stachowicz, Will Stoeckle, Alex Swerdlow, James Tanner, Marcel Torne, Quan Vuong, Anna Walling, Haohuan Wang, Blake Williams, Sukwon Yoo, Lili Yu, Ury Zhilinsky, Zhiyuan Zhou

Project name: π*0.6 / RECAP

Bibliographic source: arXiv ↗

First arXiv submission: 2025-11-18

Evidence for editorial selection: Official research report ↗

BibTeX citation
@misc{pi-star06,
  title = {π*0.6: a VLA That Learns From Experience},
  author = {{Physical Intelligence} and Ali Amin and Raichelle Aniceto and Ashwin Balakrishna and Kevin Black and Ken Conley and Grace Connors and James Darpinian and Karan Dhabalia and Jared DiCarlo and Danny Driess and Michael Equi and Adnan Esmail and Yunhao Fang and Chelsea Finn and Catherine Glossop and Thomas Godden and Ivan Goryachev and Lachy Groom and Hunter Hancock and Karol Hausman and Gashon Hussein and Brian Ichter and Szymon Jakubczak and Rowan Jen and Tim Jones and Ben Katz and Liyiming Ke and Chandra Kuchi and Marinda Lamb and Devin LeBlanc and Sergey Levine and Adrian Li-Bell and Yao Lu and Vishnu Mano and Mohith Mothukuri and Suraj Nair and Karl Pertsch and Allen Z. Ren and Charvi Sharma and Lucy Xiaoyang Shi and Laura Smith and Jost Tobias Springenberg and Kyle Stachowicz and Will Stoeckle and Alex Swerdlow and James Tanner and Marcel Torne and Quan Vuong and Anna Walling and Haohuan Wang and Blake Williams and Sukwon Yoo and Lili Yu and Ury Zhilinsky and Zhiyuan Zhou},
  year = {2025},
  url = {https://arxiv.org/abs/2511.14759}
}

Sources checked 2026-09-14

Added 2026-09-14 · Revised 2026-09-15

First page of MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception
GAMECVPR 2024

MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception

Combines active perception and modular planning to decompose and execute Minecraft tasks with contextual and procedural dependencies.

Planning & memoryLanguage & action
Details & sources

Authors: Yiran Qin, Enshen Zhou, Qichang Liu, Zhenfei Yin, Lu Sheng, Ruimao Zhang, Yu Qiao, Jing Shao

Project name: MP5

Bibliographic source: Publisher ↗

Publisher record date: 2024

BibTeX citation
@misc{mp5,
  title = {MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception},
  author = {Yiran Qin and Enshen Zhou and Qichang Liu and Zhenfei Yin and Lu Sheng and Ruimao Zhang and Yu Qiao and Jing Shao},
  year = {2024},
  url = {https://openaccess.thecvf.com/content/CVPR2024/html/Qin_MP5_A_Multi-modal_Open-ended_Embodied_System_in_Minecraft_via_Active_CVPR_2024_paper.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
EMBODIEDRSS 2023

Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware (ACT / ALOHA)

Learns fine bimanual manipulation from low-cost teleoperation demonstrations by predicting action chunks with a transformer.

Imitation learningRobot learning
Details & sources

Authors: Tony Z. Zhao, Vikash Kumar, Sergey Levine, Chelsea Finn

Project name: ACT / ALOHA

Bibliographic source: Publisher ↗

Publisher record date: 2023-07-10

BibTeX citation
@misc{act,
  title = {Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware},
  author = {Tony Z. Zhao and Vikash Kumar and Sergey Levine and Chelsea Finn},
  year = {2023},
  url = {https://roboticsproceedings.org/rss19/p016.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of COOM: A Game Benchmark for Continual Reinforcement Learning
GAMENeurIPS 2023 Datasets and Benchmarks

COOM: A Game Benchmark for Continual Reinforcement Learning

Evaluates continual reinforcement learning under sequential visual and task shifts, tracking forgetting, forward transfer, and new-task learning.

Continual learningBenchmarks
Details & sources

Authors: Tristan Tomilin, Meng Fang, Yudi Zhang, Mykola Pechenizkiy

Project name: COOM

Bibliographic source: Publisher ↗

Publisher record date: 2023-12-15

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-b01,
  title = {COOM: A Game Benchmark for Continual Reinforcement Learning},
  author = {Tristan Tomilin and Meng Fang and Yudi Zhang and Mykola Pechenizkiy},
  year = {2023},
  url = {https://proceedings.neurips.cc/paper_files/paper/2023/hash/d61d9f4fe4357296cb658795fd7999f0-Abstract-Datasets_and_Benchmarks.html},
  doi = {10.52202/075280-2965}
}

Sources checked 2026-09-14 · Knowledge-base ID B01

Added 2026-09-14

First page of HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents
GAMEICLR 2025

HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents

Separates task rewards from violation costs to evaluate visual agents under hazards such as dangerous terrain and friendly fire.

Safety & verificationBenchmarks
Details & sources

Authors: Tristan Tomilin, Meng Fang, Mykola Pechenizkiy

Project name: HASARD

Bibliographic source: Publisher ↗

Publisher record date: 2025-05-01

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-b02,
  title = {HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents},
  author = {Tristan Tomilin and Meng Fang and Mykola Pechenizkiy},
  year = {2025},
  url = {https://proceedings.iclr.cc/paper_files/paper/2025/hash/5ab848771ff8c9c47aac4128e2ef9f4e-Abstract-Conference.html}
}

Sources checked 2026-09-14 · Knowledge-base ID B02

Added 2026-09-14

First page of Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos
GAMENeurIPS 2022

Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos

Learns inverse dynamics from a small action-labeled dataset, then annotates internet videos to scale Minecraft behavioral pretraining.

Imitation learningRepresentation learning
Details & sources

Authors: Bowen Baker, Ilge Akkaya, Peter Zhokov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, Jeff Clune

Project name: Video PreTraining

Bibliographic source: Publisher ↗

Publisher record date: 2022-12-06

BibTeX citation
@misc{vpt,
  title = {Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos},
  author = {Bowen Baker and Ilge Akkaya and Peter Zhokov and Joost Huizinga and Jie Tang and Adrien Ecoffet and Brandon Houghton and Raul Sampedro and Jeff Clune},
  year = {2022},
  url = {https://proceedings.neurips.cc/paper_files/paper/2022/hash/9c7008aff45b5d8f0973b23e1a22ada0-Abstract-Conference.html},
  doi = {10.52202/068431-1789}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
EMBODIEDNeurIPS 2023 D&B

LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning

Evaluates lifelong robot learning and knowledge transfer across changes in spatial relations, objects, goals, and task combinations.

BenchmarksContinual learning
Details & sources

Authors: Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, Peter Stone

Project name: LIBERO

Bibliographic source: Publisher ↗

Publisher record date: 2023-12-15

BibTeX citation
@misc{libero,
  title = {LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning},
  author = {Bo Liu and Yifeng Zhu and Chongkai Gao and Yihao Feng and Qiang Liu and Yuke Zhu and Peter Stone},
  year = {2023},
  url = {https://proceedings.neurips.cc/paper_files/paper/2023/hash/8c3c666820ea055a77726d66fc7d447f-Abstract-Datasets_and_Benchmarks.html},
  doi = {10.52202/075280-1939}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Towards Principled Representation Learning from Videos for Reinforcement Learning
GAMEICLR 2024

Towards Principled Representation Learning from Videos for Reinforcement Learning

Studies how video pretraining without action labels affects control, including sensitivity to distracting visual dynamics.

Representation learningReinforcement learning
Details & sources

Authors: Dipendra Kumar Misra, Akanksha Saran, Tengyang Xie, Alex Lamb, John Langford

Project name: Principled Video Representations

Bibliographic source: Publisher ↗

Publisher record date: 2024-05-31

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-r01,
  title = {Towards Principled Representation Learning from Videos for Reinforcement Learning},
  author = {Dipendra Kumar Misra and Akanksha Saran and Tengyang Xie and Alex Lamb and John Langford},
  year = {2024},
  url = {https://proceedings.iclr.cc/paper_files/paper/2024/hash/eb60f103645fd14c94826665f8505a6d-Abstract-Conference.html}
}

Sources checked 2026-09-14 · Knowledge-base ID R01

Added 2026-09-14

First page of Catching Two Birds with One Stone: Reward Shaping with Dual Random Networks for Balancing Exploration and Exploitation
GAMEICML 2025

Catching Two Birds with One Stone: Reward Shaping with Dual Random Networks for Balancing Exploration and Exploitation (DuRND)

Combines novelty and reward relevance to balance exploration and exploitation in sparse-reward tasks.

Reinforcement learningExploration & skills
Details & sources

Authors: Haozhe Ma, Fangling Li, Jing Yu Lim, Zhengding Luo, Thanh Vinh Vo, Tze-Yun Leong

Project name: DuRND

Bibliographic source: Publisher ↗

Publisher record date: 2025-10-06

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-r02,
  title = {Catching Two Birds with One Stone: Reward Shaping with Dual Random Networks for Balancing Exploration and Exploitation},
  author = {Haozhe Ma and Fangling Li and Jing Yu Lim and Zhengding Luo and Thanh Vinh Vo and Tze-Yun Leong},
  year = {2025},
  url = {https://proceedings.mlr.press/v267/ma25j.html}
}

Sources checked 2026-09-14 · Knowledge-base ID R02

Added 2026-09-14

First page of Action-Sufficient State Representation Learning for Control with Structural Constraints
GAMEICML 2022

Action-Sufficient State Representation Learning for Control with Structural Constraints

Learns compact state representations that retain control-relevant factors and uses imagined transitions to support policy learning.

Representation learningWorld models
Details & sources

Authors: Biwei Huang, Chaochao Lu, Liu Leqi, Jose Miguel Hernandez-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang

Project name: Action-Sufficient Representations

Bibliographic source: Publisher ↗

Publisher record date: 2022-06-28

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-r03,
  title = {Action-Sufficient State Representation Learning for Control with Structural Constraints},
  author = {Biwei Huang and Chaochao Lu and Liu Leqi and Jose Miguel Hernandez-Lobato and Clark Glymour and Bernhard Schölkopf and Kun Zhang},
  year = {2022},
  url = {https://proceedings.mlr.press/v162/huang22f.html}
}

Sources checked 2026-09-14 · Knowledge-base ID R03

Added 2026-09-14

First page of Diffusion for World Modeling: Visual Details Matter in Atari
GAMENeurIPS 2024

Diffusion for World Modeling: Visual Details Matter in Atari (DIAMOND)

Preserves visual detail in diffusion world models for policy learning in Atari, with a separate interactive CS:GO frame-generation extension.

World modelsDiffusion models
Details & sources

Authors: Eloi Alonso, Adam Jelley, Vincent Micheli, Anssi Kanervisto, Amos Storkey, Tim Pearce, François Fleuret

Project name: DIAMOND

Bibliographic source: Publisher ↗

Publisher record date: 2024-12-16

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-w02,
  title = {Diffusion for World Modeling: Visual Details Matter in Atari},
  author = {Eloi Alonso and Adam Jelley and Vincent Micheli and Anssi Kanervisto and Amos Storkey and Tim Pearce and François Fleuret},
  year = {2024},
  url = {https://proceedings.neurips.cc/paper_files/paper/2024/hash/6bdde0373d53d4a501249547084bed43-Abstract-Conference.html},
  doi = {10.52202/079017-1873}
}

Sources checked 2026-09-14 · Knowledge-base ID W02

Added 2026-09-14

First page of StateSpaceDiffuser: Bringing Long Context to Diffusion World Models
GAMENeurIPS 2025

StateSpaceDiffuser: Bringing Long Context to Diffusion World Models

Compresses long histories with a state-space sequence model to give diffusion world models memory when revisiting scenes.

World modelsPlanning & memory
Details & sources

Authors: Nedko Savov, Naser Kazemi, Deheng Zhang, Danda Pani Paudel, Xi Wang, Luc V Gool

Project name: StateSpaceDiffuser

Bibliographic source: Publisher ↗

Publisher record date: 2026-04-23

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-w03,
  title = {StateSpaceDiffuser: Bringing Long Context to Diffusion World Models},
  author = {Nedko Savov and Naser Kazemi and Deheng Zhang and Danda Pani Paudel and Xi Wang and Luc V Gool},
  year = {2026},
  url = {https://proceedings.nips.cc/paper_files/paper/2025/hash/63943ee9fe347f3d95892cf87d9a42e6-Abstract-Conference.html},
  doi = {10.52202/085713-2316}
}

Sources checked 2026-09-14 · Knowledge-base ID W03

Added 2026-09-14

First page of EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling
GAMENeurIPS 2025

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling

Adds linear-time sequence memory to a diffusion world model, predicting images, rewards, and termination for policy learning in imagined trajectories.

World modelsPlanning & memory
Details & sources

Authors: Jia-Hua Lee, Bor-Jiun Lin, Wei-Fang Sun, Chun-Yi Lee

Project name: EDELINE

Bibliographic source: Publisher ↗

Publisher record date: 2026-04-23

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-s01,
  title = {EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling},
  author = {Jia-Hua Lee and Bor-Jiun Lin and Wei-Fang Sun and Chun-Yi Lee},
  year = {2026},
  url = {https://proceedings.neurips.cc/paper_files/paper/2025/hash/97c903fbf21a7d863af2015d8803ca8f-Abstract-Conference.html},
  doi = {10.52202/085713-3526}
}

Sources checked 2026-09-14 · Knowledge-base ID S01

Added 2026-09-14

First page of Mastering Atari Games with Limited Data
GAMENeurIPS 2021

Mastering Atari Games with Limited Data (EfficientZero)

Adds value-prefix prediction and consistency learning to MuZero, reaching strong Atari 100k performance from roughly two hours of experience.

AtariModel-based RL
Details & sources

Authors: Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, Yang Gao

Project name: EfficientZero

Bibliographic source: Publisher ↗

Publisher record date: 2021-12-06

BibTeX citation
@inproceedings{efficientzero,
  title = {Mastering Atari Games with Limited Data},
  author = {Weirui Ye and Shaohuai Liu and Thanard Kurutach and Pieter Abbeel and Yang Gao},
  booktitle = {NeurIPS 2021},
  year = {2021},
  url = {https://proceedings.neurips.cc/paper/2021/hash/d5eca8dc3820cad9fe56a3bafda65ca1-Abstract.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model
GAMEICLR 2026 Main Conference

Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model

Approximates ensemble uncertainty with a single model and uses it as an exploration signal for sparse visual navigation.

Exploration & skillsRepresentation learning
Details & sources

Authors: Moritz A. Zanger, Pascal R. Van der Vaart, Wendelin Böhmer, Matthijs T. J. Spaan

Project name: Contextual Similarity Distillation

Bibliographic source: OpenAlex ↗

Indexed publication date (OpenAlex): 2025-03-14

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-t01,
  title = {Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model},
  author = {Moritz A. Zanger and Pascal R. Van der Vaart and Wendelin Böhmer and Matthijs T. J. Spaan},
  year = {2025},
  url = {https://iclr.cc/virtual/2026/poster/10008663}
}

Sources checked 2026-09-14 · Knowledge-base ID T01

Added 2026-09-14

First page of A Generalist Agent
GAMETMLR 2022

A Generalist Agent (Gato)

Tokenizes observations and actions from hundreds of tasks so one Transformer can play games, caption images, converse, and control a robot arm.

Generalist agentsMulti-game
Details & sources

Authors: Scott Reed, Konrad Żołna, Emilio Parisotto, Sergio Gómez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Giménez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, Nando de Freitas

Project name: Gato

Bibliographic source: Publisher ↗

Publisher record date: 2022-11-10

BibTeX citation
@inproceedings{gato,
  title = {A Generalist Agent},
  author = {Scott Reed and Konrad Żołna and Emilio Parisotto and Sergio Gómez Colmenarejo and Alexander Novikov and Gabriel Barth-Maron and Mai Giménez and Yury Sulsky and Jackie Kay and Jost Tobias Springenberg and Tom Eccles and Jake Bruce and Ali Razavi and Ashley Edwards and Nicolas Heess and Yutian Chen and Raia Hadsell and Oriol Vinyals and Mahyar Bordbar and Nando de Freitas},
  booktitle = {TMLR 2022},
  year = {2022},
  url = {https://openreview.net/forum?id=1ikK0kHjvj}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Recurrent Action Transformer with Memory
GAMEICLR 2026 Main Conference

Recurrent Action Transformer with Memory (RATE)

Adds memory across segments to a decision transformer for retaining clues and predicting return-conditioned actions in long-horizon tasks.

Planning & memoryImitation learning
Details & sources

Authors: Egor Cherepanov, Alexey Staroverov, Alexey K. Kovalev, Aleksandr I. Panov

Project name: RATE

Bibliographic source: OpenAlex ↗

Indexed publication date (OpenAlex): 2023-06-15

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-t02,
  title = {Recurrent Action Transformer with Memory},
  author = {Egor Cherepanov and Alexey Staroverov and Alexey K. Kovalev and Aleksandr I. Panov},
  year = {2023},
  url = {https://iclr.cc/virtual/2026/poster/10007816}
}

Sources checked 2026-09-14 · Knowledge-base ID T02

Added 2026-09-14

First page of Human-level play in the game of Diplomacy by combining language models with strategic reasoning
GAMEScience 2022

Human-level play in the game of Diplomacy by combining language models with strategic reasoning (CICERO)

Combines strategic planning with controllable dialogue to negotiate, coordinate, and compete with people in full-press Diplomacy.

Strategy gamesLanguage agents
Details & sources

Authors: Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, Andrew Goff, Jonathan Gray, Hengyuan Hu, Athul Paul Jacob, Mojtaba Komeili, Karthik Konath, Minae Kwon, Adam Lerer, Mike Lewis, Alexander Miller, Sasha Mitts, Adithya Renduchintala, Stephen Roller, Dirk Rowe, Weiyan Shi, Joe Spisak, Alexander Wei, David Wu, Hugh Zhang, Markus Zijlstra

Project name: CICERO

Bibliographic source: Publisher ↗

Publisher record date: 2022-11-22

BibTeX citation
@inproceedings{cicero,
  title = {Human-level play in the game of Diplomacy by combining language models with strategic reasoning},
  author = {Anton Bakhtin and Noam Brown and Emily Dinan and Gabriele Farina and Colin Flaherty and Daniel Fried and Andrew Goff and Jonathan Gray and Hengyuan Hu and Athul Paul Jacob and Mojtaba Komeili and Karthik Konath and Minae Kwon and Adam Lerer and Mike Lewis and Alexander Miller and Sasha Mitts and Adithya Renduchintala and Stephen Roller and Dirk Rowe and Weiyan Shi and Joe Spisak and Alexander Wei and David Wu and Hugh Zhang and Markus Zijlstra},
  booktitle = {Science 2022},
  year = {2022},
  url = {https://doi.org/10.1126/science.ade9097}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Why the Agent Made that Decision: Contrastive Explanation Learning for Reinforcement Learning
GAMEIJCAI 2025

Why the Agent Made that Decision: Contrastive Explanation Learning for Reinforcement Learning (VisionMask)

Learns contrastive visual explanations between actions to inspect why a reinforcement-learning policy makes a decision.

Safety & verificationRepresentation learning
Details & sources

Authors: Rui Zuo, Simon Khan, Zifan Wang, Garrett Ethan Katz, Qinru Qiu

Project name: VisionMask

Bibliographic source: Publisher ↗

Publisher record date: 2025-09-16

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-c01,
  title = {Why the Agent Made that Decision: Contrastive Explanation Learning for Reinforcement Learning},
  author = {Rui Zuo and Simon Khan and Zifan Wang and Garrett Ethan Katz and Qinru Qiu},
  year = {2025},
  url = {https://www.ijcai.org/proceedings/2025/74},
  doi = {10.24963/ijcai.2025/74}
}

Sources checked 2026-09-14 · Knowledge-base ID C01

Added 2026-09-14

First page of Mastering the game of Stratego with model-free multiagent reinforcement learning
GAMEScience 2022

Mastering the game of Stratego with model-free multiagent reinforcement learning (DeepNash)

Uses regularized Nash dynamics and model-free self-play to reach expert-level Stratego without search during play.

Strategy gamesMulti-agent RL
Details & sources

Authors: Julien Pérolat, Bart De Vylder, Daniel Hennes, Eugene Tarassov, Florian Strub, Vincent C. J. de Boer, Paul Müller, Jerome T. Connor, Neil Burch, Thomas Anthony, Stephen McAleer, Romuald Élie, Sarah H. Cen, Zhe Wang, Audrūnas Gruslys, Aleksandra Malysheva, Mina Khan, Sherjil Ozair, Finbarr Timbers, Toby Pohlen, Tom Eccles, Mark Rowland, Marc Lanctot, Jean-Baptiste Lespiau, Bilal Piot, Shayegan Omidshafiei, Edward Lockhart, Laurent Sifre, Nathalie Beauguerlange, Rémi Munos, David Silver, Satinder Singh, Demis Hassabis, Karl Tuyls

Project name: DeepNash

Bibliographic source: Publisher ↗

Publisher record date: 2022-12-01

BibTeX citation
@inproceedings{deepnash,
  title = {Mastering the game of Stratego with model-free multiagent reinforcement learning},
  author = {Julien Pérolat and Bart De Vylder and Daniel Hennes and Eugene Tarassov and Florian Strub and Vincent C. J. de Boer and Paul Müller and Jerome T. Connor and Neil Burch and Thomas Anthony and Stephen McAleer and Romuald Élie and Sarah H. Cen and Zhe Wang and Audrūnas Gruslys and Aleksandra Malysheva and Mina Khan and Sherjil Ozair and Finbarr Timbers and Toby Pohlen and Tom Eccles and Mark Rowland and Marc Lanctot and Jean-Baptiste Lespiau and Bilal Piot and Shayegan Omidshafiei and Edward Lockhart and Laurent Sifre and Nathalie Beauguerlange and Rémi Munos and David Silver and Satinder Singh and Demis Hassabis and Karl Tuyls},
  booktitle = {Science 2022},
  year = {2022},
  url = {https://doi.org/10.1126/science.add4679}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Enhancing Robot Program Synthesis Through Environmental Context
GAME + EMBODIEDNeurIPS 2023

Enhancing Robot Program Synthesis Through Environmental Context (EVAPS)

Aligns program structure with visual context before and after execution to repair candidate programs in a robot domain-specific language.

Programs & toolsPlanning & memory
Details & sources

Authors: Tianyi Chen, Qidi Wang, Zhen Dong, Liwei Shen, Xin Peng

Project name: EVAPS

Bibliographic source: Publisher ↗

Publisher record date: 2023-12-15

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-c02,
  title = {Enhancing Robot Program Synthesis Through Environmental Context},
  author = {Tianyi Chen and Qidi Wang and Zhen Dong and Liwei Shen and Xin Peng},
  year = {2023},
  url = {https://proceedings.neurips.cc/paper_files/paper/2023/hash/0c1e94af650f5c74b1f3da467c2308c2-Abstract-Conference.html},
  doi = {10.52202/075280-0171}
}

Sources checked 2026-09-14 · Knowledge-base ID C02

Added 2026-09-14

First page of Transformers are Sample-Efficient World Models
GAMEICLR 2023

Transformers are Sample-Efficient World Models (IRIS)

Learns a discrete visual tokenizer and autoregressive Transformer world model, then trains an agent through imagined Atari rollouts.

World modelsAtari
Details & sources

Authors: Vincent Micheli, Eloi Alonso, François Fleuret

Project name: IRIS

Bibliographic source: Publisher ↗

Publisher record date: 2023-05-01

BibTeX citation
@inproceedings{iris,
  title = {Transformers are Sample-Efficient World Models},
  author = {Vincent Micheli and Eloi Alonso and François Fleuret},
  booktitle = {ICLR 2023},
  year = {2023},
  url = {https://openreview.net/forum?id=vhFu1Acb0xb}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Just Cluster It: An Approach for Exploration in High-Dimensions using Clustering and Pre-Trained Representations
GAMEICML 2024

Just Cluster It: An Approach for Exploration in High-Dimensions using Clustering and Pre-Trained Representations

Approximates state visitation counts by clustering visual representations, providing exploration bonuses within and across episodes.

Exploration & skillsRepresentation learning
Details & sources

Authors: Stefan Sylvius Wagner, Stefan Harmeling

Project name: Just Cluster It

Bibliographic source: Publisher ↗

Publisher record date: 2024-07-08

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-c03,
  title = {Just Cluster It: An Approach for Exploration in High-Dimensions using Clustering and Pre-Trained Representations},
  author = {Stefan Sylvius Wagner and Stefan Harmeling},
  year = {2024},
  url = {https://proceedings.mlr.press/v235/wagner24a.html}
}

Sources checked 2026-09-14 · Knowledge-base ID C03

Added 2026-09-14

First page of Genie: Generative Interactive Environments
GAMEICML 2024

Genie: Generative Interactive Environments

Learns latent actions and video dynamics from unlabeled gameplay, turning a single image or prompt into an interactable generated environment.

Generative environmentsWorld models
Details & sources

Authors: Jake Bruce, Michael Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Bechtle, Feryal Behbahani, Stephanie Chan, Nicolas Heess, Lucy González, Simon Osindero, Sherjil Ozair, Scott Reed, Jingwei Zhang, Konrad Żołna, Jeff Clune, Nando de Freitas, Satinder Singh, Tim Rocktäschel

Project name: Genie

Bibliographic source: Publisher ↗

Publisher record date: 2024-05-02

BibTeX citation
@inproceedings{genie,
  title = {Genie: Generative Interactive Environments},
  author = {Jake Bruce and Michael Dennis and Ashley Edwards and Jack Parker-Holder and Yuge Shi and Edward Hughes and Matthew Lai and Aditi Mavalankar and Richie Steigerwald and Chris Apps and Yusuf Aytar and Sarah Bechtle and Feryal Behbahani and Stephanie Chan and Nicolas Heess and Lucy González and Simon Osindero and Sherjil Ozair and Scott Reed and Jingwei Zhang and Konrad Żołna and Jeff Clune and Nando de Freitas and Satinder Singh and Tim Rocktäschel},
  booktitle = {ICML 2024},
  year = {2024},
  url = {https://proceedings.mlr.press/v235/bruce24a.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Exploration via Elliptical Episodic Bonuses
GAMENeurIPS 2022

Exploration via Elliptical Episodic Bonuses (E3B)

Constructs elliptical episodic exploration bonuses from feature coverage, using inverse dynamics to learn controllable state representations.

Exploration & skillsReinforcement learning
Details & sources

Authors: Mikael Henaff, Roberta Raileanu, Minqi Jiang, Tim Rocktäschel

Project name: E3B

Bibliographic source: Publisher ↗

Publisher record date: 2022-12-06

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-c04,
  title = {Exploration via Elliptical Episodic Bonuses},
  author = {Mikael Henaff and Roberta Raileanu and Minqi Jiang and Tim Rocktäschel},
  year = {2022},
  url = {https://proceedings.neurips.cc/paper_files/paper/2022/hash/f4f79698d48bdc1a6dec20583724182b-Abstract-Conference.html},
  doi = {10.52202/068431-2728}
}

Sources checked 2026-09-14 · Knowledge-base ID C04

Added 2026-09-14

First page of BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks
GAMENeurIPS 2023

BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks

Formalizes four fuzzy Minecraft tasks with demonstrations and dense human preferences for training and evaluating agents from human feedback.

MinecraftHuman feedback
Details & sources

Authors: Stephanie Milani, Anssi Kanervisto, Karolis Ramanauskas, Sander Schulhoff, Brandon Houghton, Rohin Shah

Project name: BEDD

Bibliographic source: Publisher ↗

Publisher record date: 2023-12-05

BibTeX citation
@inproceedings{bedd,
  title = {BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks},
  author = {Stephanie Milani and Anssi Kanervisto and Karolis Ramanauskas and Sander Schulhoff and Brandon Houghton and Rohin Shah},
  booktitle = {NeurIPS 2023},
  year = {2023},
  url = {https://proceedings.neurips.cc/paper_files/paper/2023/hash/67a6726dcd555b982cabb3446ffac01d-Abstract.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning
GAMENeurIPS 2021

Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning

Organizes reachable landmarks with successor features, combining graph planning and low-level goal policies for long-horizon navigation.

Planning & memoryExploration & skills
Details & sources

Authors: Christopher Hoang, Sungryull Sohn, Jongwook Choi, Wilka Carvalho, Honglak Lee

Project name: Successor Feature Landmarks

Bibliographic source: Publisher ↗

Publisher record date: 2021-12-06

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-c05,
  title = {Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning},
  author = {Christopher Hoang and Sungryull Sohn and Jongwook Choi and Wilka Carvalho and Honglak Lee},
  year = {2021},
  url = {https://proceedings.neurips.cc/paper_files/paper/2021/hash/e27c71957d1e6c223e0d48a165da2ee1-Abstract.html}
}

Sources checked 2026-09-14 · Knowledge-base ID C05

Added 2026-09-14

First page of Benchmarking the Spectrum of Agent Capabilities
GAMEICLR 2022

Benchmarking the Spectrum of Agent Capabilities (Crafter)

Introduces a fast open-world survival game whose achievement spectrum measures exploration, generalization, memory, and long-horizon control.

Open-worldBenchmarks
Details & sources

Authors: Danijar Hafner

Project name: Crafter

Bibliographic source: Publisher ↗

Publisher record date: 2022-04-25

BibTeX citation
@inproceedings{crafter,
  title = {Benchmarking the Spectrum of Agent Capabilities},
  author = {Danijar Hafner},
  booktitle = {ICLR 2022},
  year = {2022},
  url = {https://openreview.net/forum?id=1W0z96MFEoH}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Handling Long and Richly Constrained Tasks through Constrained Hierarchical Reinforcement Learning
GAMEAAAI 2024 — Safe, Robust and Responsible AI Track

Handling Long and Richly Constrained Tasks through Constrained Hierarchical Reinforcement Learning (CoSHRL)

Combines goal-conditioned skills and constrained path search to balance goal reaching against accumulated hazard costs over long tasks.

Planning & memorySafety & verification
Details & sources

Authors: Yuxiao Lu, Arunesh Sinha, Pradeep Varakantham

Project name: CoSHRL

Bibliographic source: OpenAlex ↗

Indexed publication date (OpenAlex): 2024-03-24

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-c06,
  title = {Handling Long and Richly Constrained Tasks through Constrained Hierarchical Reinforcement Learning},
  author = {Yuxiao Lu and Arunesh Sinha and Pradeep Varakantham},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30132}
}

Sources checked 2026-09-14 · Knowledge-base ID C06

Added 2026-09-14

First page of Multi-Game Decision Transformers
GAMENeurIPS 2022

Multi-Game Decision Transformers

Scales return-conditioned sequence modeling to one offline Transformer that plays up to 46 Atari games and adapts through fine-tuning.

Offline RLMulti-game
Details & sources

Authors: Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang, Lisa Lee, Daniel Freeman, Winnie Xu, Sergio Guadarrama, Ian Fischer, Eric Jang, Henryk Michalewski, Igor Mordatch

Project name: Multi-Game Decision Transformer

Bibliographic source: Publisher ↗

Publisher record date: 2022-12-06

BibTeX citation
@inproceedings{multi_game_dt,
  title = {Multi-Game Decision Transformers},
  author = {Kuang-Huei Lee and Ofir Nachum and Mengjiao Yang and Lisa Lee and Daniel Freeman and Winnie Xu and Sergio Guadarrama and Ian Fischer and Eric Jang and Henryk Michalewski and Igor Mordatch},
  booktitle = {NeurIPS 2022},
  year = {2022},
  url = {https://proceedings.neurips.cc/paper_files/paper/2022/hash/b2cac94f82928a85055987d9fd44753f-Abstract-Conference.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Diverse Projection Ensembles for Distributional Reinforcement Learning
GAMEICLR 2024

Diverse Projection Ensembles for Distributional Reinforcement Learning (PE-DQN)

Ensembles different return-distribution representations and uses disagreement to estimate epistemic uncertainty for exploration.

Exploration & skillsReinforcement learning
Details & sources

Authors: Moritz Akiya Zanger, Wendelin Boehmer, Matthijs T. J. Spaan

Project name: PE-DQN

Bibliographic source: Publisher ↗

Publisher record date: 2024-05-31

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-c08,
  title = {Diverse Projection Ensembles for Distributional Reinforcement Learning},
  author = {Moritz Akiya Zanger and Wendelin Boehmer and Matthijs T. J. Spaan},
  year = {2024},
  url = {https://proceedings.iclr.cc/paper_files/paper/2024/hash/cfea00ddf5a5f514bd3368d639d1933d-Abstract-Conference.html}
}

Sources checked 2026-09-14 · Knowledge-base ID C08

Added 2026-09-14

First page of Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning
GAMEICLR 2023

Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning (Diplodocus)

Regularizes self-play toward human conventions so planning agents can cooperate and compete effectively with people in no-press Diplomacy.

Strategy gamesHuman coordination
Details & sources

Authors: Anton Bakhtin, David J. Wu, Adam Lerer, Jonathan Gray, Athul Paul Jacob, Gabriele Farina, Alexander H. Miller, Noam Brown

Project name: Diplodocus

Bibliographic source: Publisher ↗

Publisher record date: 2023-05-01

BibTeX citation
@inproceedings{diplodocus,
  title = {Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning},
  author = {Anton Bakhtin and David J. Wu and Adam Lerer and Jonathan Gray and Athul Paul Jacob and Gabriele Farina and Alexander H. Miller and Noam Brown},
  booktitle = {ICLR 2023},
  year = {2023},
  url = {https://openreview.net/forum?id=F61FwJTZhb}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of A Mixture of Surprises for Unsupervised Reinforcement Learning
GAMENeurIPS 2022

A Mixture of Surprises for Unsupervised Reinforcement Learning (MOSS)

Mixes novelty-seeking and unpredictability-reducing skills during unsupervised pretraining, then adapts to downstream control tasks.

Exploration & skillsReinforcement learning
Details & sources

Authors: Andrew Zhao, Matthieu Lin, Yangguang Li, Yong-jin Liu, Gao Huang

Project name: MOSS

Bibliographic source: Publisher ↗

Publisher record date: 2022-12-06

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-c09,
  title = {A Mixture of Surprises for Unsupervised Reinforcement Learning},
  author = {Andrew Zhao and Matthieu Lin and Yangguang Li and Yong-jin Liu and Gao Huang},
  year = {2022},
  url = {https://proceedings.neurips.cc/paper_files/paper/2022/hash/a7667ee5d545a43d2f0fda98863c260e-Abstract-Conference.html},
  doi = {10.52202/068431-1891}
}

Sources checked 2026-09-14 · Knowledge-base ID C09

Added 2026-09-14

First page of Decision Transformer: Reinforcement Learning via Sequence Modeling
GAMENeurIPS 2021

Decision Transformer: Reinforcement Learning via Sequence Modeling

Recasts offline reinforcement learning as autoregressive sequence modeling conditioned on desired return, including Atari control.

Offline RLSequence modeling
Details & sources

Authors: Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch

Project name: Decision Transformer

Bibliographic source: Publisher ↗

Publisher record date: 2021-12-06

BibTeX citation
@inproceedings{decision_transformer,
  title = {Decision Transformer: Reinforcement Learning via Sequence Modeling},
  author = {Lili Chen and Kevin Lu and Aravind Rajeswaran and Kimin Lee and Aditya Grover and Misha Laskin and Pieter Abbeel and Aravind Srinivas and Igor Mordatch},
  booktitle = {NeurIPS 2021},
  year = {2021},
  url = {https://proceedings.neurips.cc/paper_files/paper/2021/hash/7f489f642a0ddb10272b5c31057f0663-Abstract.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios
GAMENeurIPS 2023

LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios

Unifies MuZero-style tree-search systems and evaluates them across board games, Atari, continuous control, MiniGrid, and GoBigger.

Tree searchBenchmarks
Details & sources

Authors: Yazhe Niu, Yuan Pu, Zhenjie Yang, Xueyan Li, Tong Zhou, Jiyuan Ren, Shuai Hu, Hongsheng Li, Yu Liu

Project name: LightZero

Bibliographic source: Publisher ↗

Publisher record date: 2023-12-10

BibTeX citation
@inproceedings{lightzero,
  title = {LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios},
  author = {Yazhe Niu and Yuan Pu and Zhenjie Yang and Xueyan Li and Tong Zhou and Jiyuan Ren and Shuai Hu and Hongsheng Li and Yu Liu},
  booktitle = {NeurIPS 2023},
  year = {2023},
  url = {https://proceedings.neurips.cc/paper_files/paper/2023/hash/765043fe026f7d704c96cec027f13843-Abstract-Datasets_and_Benchmarks.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Describe, Explain, Plan and Select: Interactive Planning with LLMs Enables Open-World Multi-Task Agents
GAMENeurIPS 2023

Describe, Explain, Plan and Select: Interactive Planning with LLMs Enables Open-World Multi-Task Agents (DEPS)

Connects execution descriptions, failure explanations, and subgoal selection to language planning, revising long-horizon Minecraft plans with feedback.

Planning & memoryLanguage & action
Details & sources

Authors: Zihao Wang, Shaofei Cai, Guanzhou Chen, Anji Liu, Xiaojian (Shawn) Ma, Yitao Liang

Project name: DEPS

Bibliographic source: Publisher ↗

Publisher record date: 2023-12-15

BibTeX citation
@misc{deps,
  title = {Describe, Explain, Plan and Select: Interactive Planning with LLMs Enables Open-World Multi-Task Agents},
  author = {Zihao Wang and Shaofei Cai and Guanzhou Chen and Anji Liu and Xiaojian (Shawn) Ma and Yitao Liang},
  year = {2023},
  url = {https://proceedings.neurips.cc/paper_files/paper/2023/hash/6b8dfb8c0c12e6fafc6c256cb08a5ca7-Abstract-Conference.html},
  doi = {10.52202/075280-1480}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
EMBODIEDCoRL 2022

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (SayCan)

Scores language-model plans by the robot skills that are actually feasible, grounding long-horizon instructions in learned affordances.

Language planningAffordances
Details & sources

Authors: Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Daniel Ho, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Eric Jang, Rosario Jauregui Ruano, Kyle Jeffrey, Sally Jesmonth, Nikhil Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Kuang-Huei Lee, Sergey Levine, Yao Lu, Linda Luu, Carolina Parada, Peter Pastor, Jornell Quiambao, Kanishka Rao, Jarek Rettinghouse, Diego Reyes, Pierre Sermanet, Nicolas Sievers, Clayton Tan, Alexander Toshev, Vincent Vanhoucke, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Mengyuan Yan, Andy Zeng

Project name: SayCan

Bibliographic source: Publisher ↗

Publisher record date: 2023-03-06

BibTeX citation
@inproceedings{saycan,
  title = {Do As I Can, Not As I Say: Grounding Language in Robotic Affordances},
  author = {Michael Ahn and Anthony Brohan and Noah Brown and Yevgen Chebotar and Omar Cortes and Byron David and Chelsea Finn and Chuyuan Fu and Keerthana Gopalakrishnan and Karol Hausman and Alex Herzog and Daniel Ho and Jasmine Hsu and Julian Ibarz and Brian Ichter and Alex Irpan and Eric Jang and Rosario Jauregui Ruano and Kyle Jeffrey and Sally Jesmonth and Nikhil Joshi and Ryan Julian and Dmitry Kalashnikov and Yuheng Kuang and Kuang-Huei Lee and Sergey Levine and Yao Lu and Linda Luu and Carolina Parada and Peter Pastor and Jornell Quiambao and Kanishka Rao and Jarek Rettinghouse and Diego Reyes and Pierre Sermanet and Nicolas Sievers and Clayton Tan and Alexander Toshev and Vincent Vanhoucke and Fei Xia and Ted Xiao and Peng Xu and Sichun Xu and Mengyuan Yan and Andy Zeng},
  booktitle = {CoRL 2022},
  year = {2022},
  url = {https://proceedings.mlr.press/v205/ichter23a.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of PaLM-E: An Embodied Multimodal Language Model
EMBODIEDICML 2023

PaLM-E: An Embodied Multimodal Language Model

Injects visual and continuous sensor embeddings into a large language model for robot planning, visual reasoning, and transfer across embodiments.

Embodied language modelsMultimodal reasoning
Details & sources

Authors: Danny Driess, Fei Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, Pete Florence

Project name: PaLM-E

Bibliographic source: Publisher ↗

Publisher record date: 2023-07-03

BibTeX citation
@inproceedings{palm_e,
  title = {PaLM-E: An Embodied Multimodal Language Model},
  author = {Danny Driess and Fei Xia and Mehdi S. M. Sajjadi and Corey Lynch and Aakanksha Chowdhery and Brian Ichter and Ayzaan Wahid and Jonathan Tompson and Quan Vuong and Tianhe Yu and Wenlong Huang and Yevgen Chebotar and Pierre Sermanet and Daniel Duckworth and Sergey Levine and Vincent Vanhoucke and Karol Hausman and Marc Toussaint and Klaus Greff and Andy Zeng and Igor Mordatch and Pete Florence},
  booktitle = {ICML 2023},
  year = {2023},
  url = {https://proceedings.mlr.press/v202/driess23a.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of RT-1: Robotics Transformer for Real-World Control at Scale
EMBODIEDRSS 2023

RT-1: Robotics Transformer for Real-World Control at Scale

Trains an efficient Transformer policy on 130,000 real-robot episodes and more than 700 language-conditioned tasks.

Robot transformersMulti-task learning
Details & sources

Authors: Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alexander Herzog, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Sally Jesmonth, Nikhil Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, Kuang-Huei Lee, Sergey Levine, Yao Lu, Utsav Malla, Deeksha Manjunath, Igor Mordatch, Ofir Nachum, Carolina Parada, Jodilyn Peralta, Emily Perez, Karl Pertsch, Jornell Quiambao, Kanishka Rao, Michael Ryoo, Grecia Salazar, Pannag Sanketi, Kevin Sayed, Jaspiar Singh, Sumedh Sontakke, Austin Stone, Clayton Tan, Huong Tran, Vincent Vanhoucke, Steve Vega, Quan Vuong, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Tianhe Yu, Brianna Zitkovich

Project name: RT-1

Bibliographic source: Publisher ↗

Publisher record date: 2023-07-10

BibTeX citation
@inproceedings{rt1,
  title = {RT-1: Robotics Transformer for Real-World Control at Scale},
  author = {Anthony Brohan and Noah Brown and Justice Carbajal and Yevgen Chebotar and Joseph Dabis and Chelsea Finn and Keerthana Gopalakrishnan and Karol Hausman and Alexander Herzog and Jasmine Hsu and Julian Ibarz and Brian Ichter and Alex Irpan and Tomas Jackson and Sally Jesmonth and Nikhil Joshi and Ryan Julian and Dmitry Kalashnikov and Yuheng Kuang and Isabel Leal and Kuang-Huei Lee and Sergey Levine and Yao Lu and Utsav Malla and Deeksha Manjunath and Igor Mordatch and Ofir Nachum and Carolina Parada and Jodilyn Peralta and Emily Perez and Karl Pertsch and Jornell Quiambao and Kanishka Rao and Michael Ryoo and Grecia Salazar and Pannag Sanketi and Kevin Sayed and Jaspiar Singh and Sumedh Sontakke and Austin Stone and Clayton Tan and Huong Tran and Vincent Vanhoucke and Steve Vega and Quan Vuong and Fei Xia and Ted Xiao and Peng Xu and Sichun Xu and Tianhe Yu and Brianna Zitkovich},
  booktitle = {RSS 2023},
  year = {2023},
  url = {https://roboticsproceedings.org/rss19/p025.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of STEVE-1: A Generative Model for Text-to-Behavior in Minecraft
GAMENeurIPS 2023

STEVE-1: A Generative Model for Text-to-Behavior in Minecraft

Conditions a VPT behavior model on text and visual goals to follow short-horizon, open-ended instructions in Minecraft.

Language & actionImitation learning
Details & sources

Authors: Shalev Lifshitz, Keiran Paster, Harris Chan, Jimmy Ba, Sheila McIlraith

Project name: STEVE-1

Bibliographic source: Publisher ↗

Publisher record date: 2023-12-15

BibTeX citation
@misc{steve1,
  title = {STEVE-1: A Generative Model for Text-to-Behavior in Minecraft},
  author = {Shalev Lifshitz and Keiran Paster and Harris Chan and Jimmy Ba and Sheila McIlraith},
  year = {2023},
  url = {https://proceedings.neurips.cc/paper_files/paper/2023/hash/dd03f856fc7f2efeec8b1c796284561d-Abstract-Conference.html},
  doi = {10.52202/075280-3064}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of VIMA: General Robot Manipulation with Multimodal Prompts
EMBODIEDICML 2023

VIMA: General Robot Manipulation with Multimodal Prompts

Expresses many simulated manipulation tasks as prompts that interleave language, images, and demonstrations, then predicts robot actions autoregressively.

Multimodal promptingManipulation
Details & sources

Authors: Yunfan Jiang, Agrim Gupta, Zichen Zhang, Guanzhi Wang, Yongqiang Dou, Yanjun Chen, Li Fei-Fei, Anima Anandkumar, Yuke Zhu, Linxi Fan

Project name: VIMA

Bibliographic source: Publisher ↗

Publisher record date: 2023-07-23

BibTeX citation
@inproceedings{vima,
  title = {VIMA: General Robot Manipulation with Multimodal Prompts},
  author = {Yunfan Jiang and Agrim Gupta and Zichen Zhang and Guanzhi Wang and Yongqiang Dou and Yanjun Chen and Li Fei-Fei and Anima Anandkumar and Yuke Zhu and Linxi Fan},
  booktitle = {ICML 2023},
  year = {2023},
  url = {https://proceedings.mlr.press/v202/jiang23b.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation
EMBODIEDCoRL 2022

Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation (PerAct)

Uses a Perceiver over voxelized RGB-D observations to detect language-conditioned 6-DoF actions for many tasks with one policy.

3D policiesLanguage conditioning
Details & sources

Authors: Mohit Shridhar, Lucas Manuelli, Dieter Fox

Project name: PerAct

Bibliographic source: Publisher ↗

Publisher record date: 2023-03-06

BibTeX citation
@inproceedings{peract,
  title = {Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation},
  author = {Mohit Shridhar and Lucas Manuelli and Dieter Fox},
  booktitle = {CoRL 2022},
  year = {2022},
  url = {https://proceedings.mlr.press/v205/shridhar23a.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of R3M: A Universal Visual Representation for Robot Manipulation
EMBODIEDCoRL 2022

R3M: A Universal Visual Representation for Robot Manipulation

Pretrains a reusable robot visual encoder on Ego4D human video through temporal, language, and sparsity objectives.

Visual pretrainingRobot learning
Details & sources

Authors: Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, Abhinav Gupta

Project name: R3M

Bibliographic source: Publisher ↗

Publisher record date: 2023-03-06

BibTeX citation
@inproceedings{r3m,
  title = {R3M: A Universal Visual Representation for Robot Manipulation},
  author = {Suraj Nair and Aravind Rajeswaran and Vikash Kumar and Chelsea Finn and Abhinav Gupta},
  booktitle = {CoRL 2022},
  year = {2022},
  url = {https://proceedings.mlr.press/v205/nair23a.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?
EMBODIEDNeurIPS 2023

Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence? (VC-1)

Builds CortexBench and VC-1 to compare large pretrained visual representations across navigation, locomotion, and manipulation.

Embodied visionBenchmarks
Details & sources

Authors: Arjun Majumdar, Karmesh Yadav, Sergio Arnaud, Jason Ma, Claire Chen, Sneha Silwal, Aryan Jain, Vincent-Pierre Berges, Tingfan Wu, Jay Vakil, Pieter Abbeel, Jitendra Malik, Dhruv Batra, Yixin Lin, Oleksandr Maksymets, Aravind Rajeswaran, Franziska Meier

Project name: VC-1

Bibliographic source: Publisher ↗

Publisher record date: 2023-12-10

BibTeX citation
@inproceedings{vc1,
  title = {Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?},
  author = {Arjun Majumdar and Karmesh Yadav and Sergio Arnaud and Jason Ma and Claire Chen and Sneha Silwal and Aryan Jain and Vincent-Pierre Berges and Tingfan Wu and Jay Vakil and Pieter Abbeel and Jitendra Malik and Dhruv Batra and Yixin Lin and Oleksandr Maksymets and Aravind Rajeswaran and Franziska Meier},
  booktitle = {NeurIPS 2023},
  year = {2023},
  url = {https://proceedings.neurips.cc/paper_files/paper/2023/hash/022ca1bed6b574b962c48a2856eb207b-Abstract-Conference.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Octo: An Open-Source Generalist Robot Policy
EMBODIEDRSS 2024

Octo: An Open-Source Generalist Robot Policy

Pretrains a generalist policy on demonstrations from multiple robots, with language or goal-image conditioning and adaptation to new inputs and action spaces.

Robot learningVision-language-action
Details & sources

Authors: Dibya Ghosh, Homer Rich Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Tobias Kreiman, Charles Xu, Jianlan Luo, You Liang Tan, Lawrence Yunliang Chen, Quan Vuong, Ted Xiao, Pannag R Sanketi, Dorsa Sadigh, Chelsea Finn, Sergey Levine

Project name: Octo

Bibliographic source: Publisher ↗

Publisher record date: 2024-07-15

BibTeX citation
@misc{octo,
  title = {Octo: An Open-Source Generalist Robot Policy},
  author = {Dibya Ghosh and Homer Rich Walke and Karl Pertsch and Kevin Black and Oier Mees and Sudeep Dasari and Joey Hejna and Tobias Kreiman and Charles Xu and Jianlan Luo and You Liang Tan and Lawrence Yunliang Chen and Quan Vuong and Ted Xiao and Pannag R Sanketi and Dorsa Sadigh and Chelsea Finn and Sergey Levine},
  year = {2024},
  url = {https://www.roboticsproceedings.org/rss20/p090.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation
EMBODIEDTMLR 2024

RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation

Trains one goal-conditioned Transformer across robot arms, then uses its own successful rollouts to expand data for new tasks and embodiments.

Generalist policiesSelf-improvement
Details & sources

Authors: Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Devin, Alex Lee, Maria Bauzá, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, Antoine Laurens, Claudio Fantacci, Valentin Dalibard, Martina Zambelli, Murilo Martins, Rugile Pevceviciute, Michiel Blokzijl, Misha Denil, Nathan Batchelor, Thomas Lampe, Emilio Parisotto, Konrad Żołna, Scott Reed, Sergio Gómez Colmenarejo, Jon Scholz, Abbas Abdolmaleki, Oliver Groth, Jean-Baptiste Regli, Oleg Sushkov, Tom Rothörl, José Enrique Chen, Yusuf Aytar, Dave Barker, Joy Ortiz, Martin Riedmiller, Jost Tobias Springenberg, Raia Hadsell, Francesco Nori, Nicolas Heess

Project name: RoboCat

Bibliographic source: Publisher ↗

Publisher record date: 2024-02-07

BibTeX citation
@inproceedings{robocat,
  title = {RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation},
  author = {Konstantinos Bousmalis and Giulia Vezzani and Dushyant Rao and Coline Devin and Alex Lee and Maria Bauzá and Todor Davchev and Yuxiang Zhou and Agrim Gupta and Akhil Raju and Antoine Laurens and Claudio Fantacci and Valentin Dalibard and Martina Zambelli and Murilo Martins and Rugile Pevceviciute and Michiel Blokzijl and Misha Denil and Nathan Batchelor and Thomas Lampe and Emilio Parisotto and Konrad Żołna and Scott Reed and Sergio Gómez Colmenarejo and Jon Scholz and Abbas Abdolmaleki and Oliver Groth and Jean-Baptiste Regli and Oleg Sushkov and Tom Rothörl and José Enrique Chen and Yusuf Aytar and Dave Barker and Joy Ortiz and Martin Riedmiller and Jost Tobias Springenberg and Raia Hadsell and Francesco Nori and Nicolas Heess},
  booktitle = {TMLR 2024},
  year = {2024},
  url = {https://openreview.net/forum?id=vsCpLiIWHu}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
EMBODIEDCoRL 2023

RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Represents robot actions as tokens and co-trains them with vision-language tasks to study transfer of web knowledge into manipulation.

Vision-language-actionRobot learning
Details & sources

Authors: Brianna Zitkovich, Tianhe Yu, Sichun Xu, Peng Xu, Ted Xiao, Fei Xia, Jialin Wu, Paul Wohlhart, Stefan Welker, Ayzaan Wahid, Quan Vuong, Vincent Vanhoucke, Huong Tran, Radu Soricut, Anikait Singh, Jaspiar Singh, Pierre Sermanet, Pannag R. Sanketi, Grecia Salazar, Michael S. Ryoo, Krista Reymann, Kanishka Rao, Karl Pertsch, Igor Mordatch, Henryk Michalewski, Yao Lu, Sergey Levine, Lisa Lee, Tsang-Wei Edward Lee, Isabel Leal, Yuheng Kuang, Dmitry Kalashnikov, Ryan Julian, Nikhil J. Joshi, Alex Irpan, Brian Ichter, Jasmine Hsu, Alexander Herzog, Karol Hausman, Keerthana Gopalakrishnan, Chuyuan Fu, Pete Florence, Chelsea Finn, Kumar Avinava Dubey, Danny Driess, Tianli Ding, Krzysztof Marcin Choromanski, Xi Chen, Yevgen Chebotar, Justice Carbajal, Noah Brown, Anthony Brohan, Montserrat Gonzalez Arenas, Kehang Han

Project name: RT-2

Bibliographic source: Publisher ↗

Publisher record date: 2023-12-02

BibTeX citation
@misc{rt2,
  title = {RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control},
  author = {Brianna Zitkovich and Tianhe Yu and Sichun Xu and Peng Xu and Ted Xiao and Fei Xia and Jialin Wu and Paul Wohlhart and Stefan Welker and Ayzaan Wahid and Quan Vuong and Vincent Vanhoucke and Huong Tran and Radu Soricut and Anikait Singh and Jaspiar Singh and Pierre Sermanet and Pannag R. Sanketi and Grecia Salazar and Michael S. Ryoo and Krista Reymann and Kanishka Rao and Karl Pertsch and Igor Mordatch and Henryk Michalewski and Yao Lu and Sergey Levine and Lisa Lee and Tsang-Wei Edward Lee and Isabel Leal and Yuheng Kuang and Dmitry Kalashnikov and Ryan Julian and Nikhil J. Joshi and Alex Irpan and Brian Ichter and Jasmine Hsu and Alexander Herzog and Karol Hausman and Keerthana Gopalakrishnan and Chuyuan Fu and Pete Florence and Chelsea Finn and Kumar Avinava Dubey and Danny Driess and Tianli Ding and Krzysztof Marcin Choromanski and Xi Chen and Yevgen Chebotar and Justice Carbajal and Noah Brown and Anthony Brohan and Montserrat Gonzalez Arenas and Kehang Han},
  year = {2023},
  url = {https://proceedings.mlr.press/v229/zitkovich23a.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Mobile ALOHA: Learning Bimanual Mobile Manipulation using Low-Cost Whole-Body Teleoperation
EMBODIEDCoRL 2024

Mobile ALOHA: Learning Bimanual Mobile Manipulation using Low-Cost Whole-Body Teleoperation

Extends ALOHA with a mobile base and whole-body teleoperation, enabling imitation of long-horizon bimanual household tasks.

Mobile manipulationBimanual control
Details & sources

Authors: Zipeng Fu, Tony Z. Zhao, Chelsea Finn

Project name: Mobile ALOHA

Bibliographic source: Publisher ↗

Publisher record date: 2025-01-12

BibTeX citation
@inproceedings{mobile_aloha,
  title = {Mobile ALOHA: Learning Bimanual Mobile Manipulation using Low-Cost Whole-Body Teleoperation},
  author = {Zipeng Fu and Tony Z. Zhao and Chelsea Finn},
  booktitle = {CoRL 2024},
  year = {2024},
  url = {https://proceedings.mlr.press/v270/fu25b.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models
EMBODIEDCoRL 2023

VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models

Uses language models to compose perception outputs and 3D value maps into objectives and constraints for robot motion planning.

Programs & toolsPlanning & memory
Details & sources

Authors: Wenlong Huang, Chen Wang, Ruohan Zhang, Yunzhu Li, Jiajun Wu, Li Fei-Fei

Project name: VoxPoser

Bibliographic source: Publisher ↗

Publisher record date: 2023-12-02

BibTeX citation
@misc{voxposer,
  title = {VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models},
  author = {Wenlong Huang and Chen Wang and Ruohan Zhang and Yunzhu Li and Jiajun Wu and Li Fei-Fei},
  year = {2023},
  url = {https://proceedings.mlr.press/v229/huang23b.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation
EMBODIEDICLR 2024

Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation (GR-1)

Pretrains one GPT-style model to predict video, then fine-tunes it to emit actions and future images for multi-task robot manipulation.

Video pretrainingGenerative policies
Details & sources

Authors: Hongtao Wu, Ya Jing, Chilam Cheang, Guangzeng Chen, Jiafeng Xu, Xinghang Li, Minghuan Liu, Hang Li, Tao Kong

Project name: GR-1

Bibliographic source: Publisher ↗

Publisher record date: 2024-05-07

BibTeX citation
@inproceedings{gr1,
  title = {Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation},
  author = {Hongtao Wu and Ya Jing and Chilam Cheang and Guangzeng Chen and Jiafeng Xu and Xinghang Li and Minghuan Liu and Hang Li and Tao Kong},
  booktitle = {ICLR 2024},
  year = {2024},
  url = {https://proceedings.iclr.cc/paper_files/paper/2024/hash/2c37c5bcef24b9541550261dcd63261b-Abstract-Conference.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation
EMBODIEDCoRL 2024

ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation

Translates instructions into spatial and temporal constraints between 3D keypoints for optimization-based, multi-stage robot manipulation.

Programs & toolsPlanning & memory
Details & sources

Authors: Wenlong Huang, Chen Wang, Yunzhu Li, Ruohan Zhang, Li Fei-Fei

Project name: ReKep

Bibliographic source: Publisher ↗

Publisher record date: 2025-01-12

Publication note: Conference year: 2024. PMLR publication year: 2025.

BibTeX citation
@misc{rekep,
  title = {ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation},
  author = {Wenlong Huang and Chen Wang and Yunzhu Li and Ruohan Zhang and Li Fei-Fei},
  year = {2025},
  url = {https://proceedings.mlr.press/v270/huang25g.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of 3D Diffuser Actor: Policy Diffusion with 3D Scene Representations
EMBODIEDCoRL 2024

3D Diffuser Actor: Policy Diffusion with 3D Scene Representations

Denoises 3D end-effector trajectories while attending to scene geometry, language, and proprioception for multi-task manipulation.

3D policiesDiffusion models
Details & sources

Authors: Tsung-Wei Ke, Nikolaos Gkanatsios, Katerina Fragkiadaki

Project name: 3D Diffuser Actor

Bibliographic source: Publisher ↗

Publisher record date: 2025-01-12

BibTeX citation
@inproceedings{3d_diffuser_actor,
  title = {3D Diffuser Actor: Policy Diffusion with 3D Scene Representations},
  author = {Tsung-Wei Ke and Nikolaos Gkanatsios and Katerina Fragkiadaki},
  booktitle = {CoRL 2024},
  year = {2024},
  url = {https://proceedings.mlr.press/v270/ke25a.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of 3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations
EMBODIEDRSS 2024

3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations (DP3)

Pairs compact point-cloud encodings with diffusion policies to improve low-demo learning and spatial generalization across dexterous tasks.

3D policiesImitation learning
Details & sources

Authors: Yanjie Ze, Gu Zhang, Kangning Zhang, Chenyuan Hu, Muhan Wang, Huazhe Xu

Project name: DP3

Bibliographic source: Publisher ↗

Publisher record date: 2024-07-15

BibTeX citation
@inproceedings{dp3,
  title = {3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations},
  author = {Yanjie Ze and Gu Zhang and Kangning Zhang and Chenyuan Hu and Muhan Wang and Huazhe Xu},
  booktitle = {RSS 2024},
  year = {2024},
  url = {https://www.roboticsproceedings.org/rss20/p067.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of CLIPort: What and Where Pathways for Robotic Manipulation
EMBODIEDCoRL 2021

CLIPort: What and Where Pathways for Robotic Manipulation

Combines CLIP semantics with Transporter-style spatial precision for language-conditioned tabletop manipulation.

Vision-languageManipulation
Details & sources

Authors: Mohit Shridhar, Lucas Manuelli, Dieter Fox

Project name: CLIPort

Bibliographic source: Publisher ↗

Publisher record date: 2022-01-11

BibTeX citation
@inproceedings{cliport,
  title = {CLIPort: What and Where Pathways for Robotic Manipulation},
  author = {Mohit Shridhar and Lucas Manuelli and Dieter Fox},
  booktitle = {CoRL 2021},
  year = {2021},
  url = {https://proceedings.mlr.press/v164/shridhar22a.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents
EMBODIEDICML 2025

EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents

Systematically evaluates multimodal language models on decision-making in visually grounded embodied tasks.

BenchmarksLanguage & action
Details & sources

Authors: Rui Yang, Hanyang Chen, Junyu Zhang, Mark Zhao, Cheng Qian, Kangrui Wang, Qineng Wang, Teja Venkat Koripella, Marziyeh Movahedi, Manling Li, Heng Ji, Huan Zhang, Tong Zhang

Project name: EmbodiedBench

Bibliographic source: Publisher ↗

Publisher record date: 2025-10-06

BibTeX citation
@misc{embodiedbench,
  title = {EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents},
  author = {Rui Yang and Hanyang Chen and Junyu Zhang and Mark Zhao and Cheng Qian and Kangrui Wang and Qineng Wang and Teja Venkat Koripella and Marziyeh Movahedi and Manling Li and Heng Ji and Huan Zhang and Tong Zhang},
  year = {2025},
  url = {https://proceedings.mlr.press/v267/yang25f.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Inner Monologue: Embodied Reasoning through Planning with Language Models
EMBODIEDCoRL 2022

Inner Monologue: Embodied Reasoning through Planning with Language Models

Feeds success detectors, scene descriptions, and human feedback back into an LLM planner so robot plans can react to execution outcomes.

Language planningClosed-loop reasoning
Details & sources

Authors: Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, Pierre Sermanet, Noah Brown, Tomas Jackson, Linda Luu, Sergey Levine, Karol Hausman, Brian Ichter

Project name: Inner Monologue

Bibliographic source: Publisher ↗

Publisher record date: 2023-03-06

BibTeX citation
@inproceedings{inner_monologue,
  title = {Inner Monologue: Embodied Reasoning through Planning with Language Models},
  author = {Wenlong Huang and Fei Xia and Ted Xiao and Harris Chan and Jacky Liang and Pete Florence and Andy Zeng and Jonathan Tompson and Igor Mordatch and Yevgen Chebotar and Pierre Sermanet and Noah Brown and Tomas Jackson and Linda Luu and Sergey Levine and Karol Hausman and Brian Ichter},
  booktitle = {CoRL 2022},
  year = {2022},
  url = {https://proceedings.mlr.press/v205/huang23c.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Consistency Policy: Accelerated Visuomotor Policies via Consistency Distillation
EMBODIEDRSS 2024

Consistency Policy: Accelerated Visuomotor Policies via Consistency Distillation

Distills diffusion policies into fewer action-generation steps to study visual-control latency under limited compute.

Diffusion modelsRobot learning
Details & sources

Authors: Aaditya Prasad, Kevin Lin, Jimmy Wu, Linqi Zhou, Jeannette Bohg

Project name: Consistency Policy

Bibliographic source: Publisher ↗

Publisher record date: 2024-07-15

BibTeX citation
@misc{consistency-policy,
  title = {Consistency Policy: Accelerated Visuomotor Policies via Consistency Distillation},
  author = {Aaditya Prasad and Kevin Lin and Jimmy Wu and Linqi Zhou and Jeannette Bohg},
  year = {2024},
  url = {https://www.roboticsproceedings.org/rss20/p071.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches
EMBODIEDICLR 2024

RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches

Conditions robot policies on rough trajectory sketches that can come from drawings, videos, or generators, enabling new motion-centric tasks.

Task conditioningGeneralization
Details & sources

Authors: Jiayuan Gu, Sean Kirmani, Paul Wohlhart, Yao Lu, Montserrat Gonzalez Arenas, Kanishka Rao, Wenhao Yu, Chuyuan Fu, Keerthana Gopalakrishnan, Zhuo Xu, Priya Sundaresan, Peng Xu, Hao Su, Karol Hausman, Chelsea Finn, Quan Vuong, Ted Xiao

Project name: RT-Trajectory

Bibliographic source: Publisher ↗

Publisher record date: 2024-05-07

BibTeX citation
@inproceedings{rt_trajectory,
  title = {RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches},
  author = {Jiayuan Gu and Sean Kirmani and Paul Wohlhart and Yao Lu and Montserrat Gonzalez Arenas and Kanishka Rao and Wenhao Yu and Chuyuan Fu and Keerthana Gopalakrishnan and Zhuo Xu and Priya Sundaresan and Peng Xu and Hao Su and Karol Hausman and Chelsea Finn and Quan Vuong and Ted Xiao},
  booktitle = {ICLR 2024},
  year = {2024},
  url = {https://proceedings.iclr.cc/paper_files/paper/2024/hash/c383e44d9a878d1982d9abb838bd5d8a-Abstract-Conference.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of BAKU: An Efficient Transformer for Multi-Task Policy Learning
EMBODIEDNeurIPS 2024

BAKU: An Efficient Transformer for Multi-Task Policy Learning

Uses transformers for multitask robot policies, studying efficient architectures and action-prediction designs.

Robot learningImitation learning
Details & sources

Authors: Siddhant Haldar, Zhuoran Peng, Lerrel Pinto

Project name: BAKU

Bibliographic source: Publisher ↗

Publisher record date: 2024-12-16

BibTeX citation
@misc{baku,
  title = {BAKU: An Efficient Transformer for Multi-Task Policy Learning},
  author = {Siddhant Haldar and Zhuoran Peng and Lerrel Pinto},
  year = {2024},
  url = {https://proceedings.neurips.cc/paper_files/paper/2024/hash/ff887781480973bd3cb6026feb378d1e-Abstract-Conference.html},
  doi = {10.52202/079017-4484}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Code as Policies: Language Model Programs for Embodied Control
EMBODIEDICRA 2023

Code as Policies: Language Model Programs for Embodied Control

Prompts language models to write executable policy code that composes perception and control APIs for spatial and long-horizon robot tasks.

Program synthesisLanguage planning
Details & sources

Authors: Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, Andy Zeng

Project name: Code as Policies

Bibliographic source: Publisher ↗

Publisher record date: 2023-05-29

BibTeX citation
@inproceedings{code_as_policies,
  title = {Code as Policies: Language Model Programs for Embodied Control},
  author = {Jacky Liang and Wenlong Huang and Fei Xia and Peng Xu and Karol Hausman and Brian Ichter and Pete Florence and Andy Zeng},
  booktitle = {ICRA 2023},
  year = {2023},
  url = {https://doi.org/10.1109/ICRA48891.2023.10160591}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of VLA-Cache: Efficient Vision-Language-Action Manipulation via Adaptive Token Caching
EMBODIEDNeurIPS 2025

VLA-Cache: Efficient Vision-Language-Action Manipulation via Adaptive Token Caching

Reuses visual-token computation to accelerate vision-language-action models, examining the tradeoff between latency and manipulation success.

Vision-language-actionRobot learning
Details & sources

Authors: Siyu Xu, Yunke Wang, Chenghao Xia, Dihao Zhu, Tao Huang, Chang Xu

Project name: VLA-Cache

Bibliographic source: Publisher ↗

Publisher record date: 2026-04-23

BibTeX citation
@misc{vla-cache,
  title = {VLA-Cache: Efficient Vision-Language-Action Manipulation via Adaptive Token Caching},
  author = {Siyu Xu and Yunke Wang and Chenghao Xia and Dihao Zhu and Tao Huang and Chang Xu},
  year = {2026},
  url = {https://proceedings.neurips.cc/paper_files/paper/2025/hash/f062da1973ac9ac61fc6d44dd7fa309f-Abstract-Conference.html},
  doi = {10.52202/085713-5484}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of RoboChemist: Long-Horizon and Safety-Compliant Robotic Chemical Experimentation
EMBODIEDCoRL 2025

RoboChemist: Long-Horizon and Safety-Compliant Robotic Chemical Experimentation

Combines high-level vision-language planning and low-level action models for multi-stage laboratory tasks, with feedback and procedural constraints.

Planning & memorySafety & verification
Details & sources

Authors: Zongzheng Zhang, Chenghao Yue, Haobo Xu, Minwen Liao, Xianglin Qi, Huan-ang Gao, Ziwei Wang, Hao Zhao

Project name: RoboChemist

Bibliographic source: Publisher ↗

Publisher record date: 2025-10-07

BibTeX citation
@misc{robochemist,
  title = {RoboChemist: Long-Horizon and Safety-Compliant Robotic Chemical Experimentation},
  author = {Zongzheng Zhang and Chenghao Yue and Haobo Xu and Minwen Liao and Xianglin Qi and Huan-ang Gao and Ziwei Wang and Hao Zhao},
  year = {2025},
  url = {https://proceedings.mlr.press/v305/zhang25i.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Foundation models in robotics: Applications, challenges, and the future
EMBODIEDIJRR 2025

Foundation models in robotics: Applications, challenges, and the future

Reviews how foundation models enter robot perception, planning, and control, with a research agenda spanning data, uncertainty, safety, latency, and reproducibility.

SurveysRobot learning
Details & sources

Authors: Roya Firoozi, Johnathan Tucker, Stephen Tian, Anirudha Majumdar, Jiankai Sun, Weiyu Liu, Yuke Zhu, Shuran Song, Ashish Kapoor, Karol Hausman, Brian Ichter, Danny Driess, Jiajun Wu, Cewu Lu, Mac Schwager

Project name: Foundation models in robotics

Bibliographic source: Crossref ↗

Publication date (Crossref): 2024-09-25

Credit: Roya Firoozi and collaborators

BibTeX citation
@misc{survey-foundation-robotics,
  title = {Foundation models in robotics: Applications, challenges, and the future},
  author = {Roya Firoozi and Johnathan Tucker and Stephen Tian and Anirudha Majumdar and Jiankai Sun and Weiyu Liu and Yuke Zhu and Shuran Song and Ashish Kapoor and Karol Hausman and Brian Ichter and Danny Driess and Jiajun Wu and Cewu Lu and Mac Schwager},
  year = {2024},
  url = {https://journals.sagepub.com/doi/10.1177/02783649241281508},
  doi = {10.1177/02783649241281508}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of What foundation models can bring for robot learning in manipulation: A survey
EMBODIEDIJRR 2026

What foundation models can bring for robot learning in manipulation: A survey+2

Organizes foundation models into a modular framework for general manipulation and examines the opportunities and limits of each role.

SurveysRobot learning
Details & sources

Authors: Dingzhe Li, Yixiang Jin, YuHao Sun, Yong A, Hongze Yu, Jun Shi, Xiaoshuai Hao, Peng Hao, Huaping Liu, Xiang Li, Xinde Li, Fuchun Sun, Jianwei Zhang, Bin Fang

Project name: Foundation models for manipulation

PDF preview version: arXiv version

Bibliographic source: Crossref ↗

Publication date (Crossref): 2025-11-20

Credit: Dingzhe Li and collaborators

Publication note: First published online on 2025-11-20; the journal issue is dated 2026.

BibTeX citation
@misc{survey-foundation-manipulation,
  title = {What foundation models can bring for robot learning in manipulation: A survey},
  author = {Dingzhe Li and Yixiang Jin and YuHao Sun and Yong A and Hongze Yu and Jun Shi and Xiaoshuai Hao and Peng Hao and Huaping Liu and Xiang Li and Xinde Li and Fuchun Sun and Jianwei Zhang and Bin Fang},
  year = {2025},
  url = {https://journals.sagepub.com/doi/10.1177/02783649251390579},
  doi = {10.1177/02783649251390579}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge
GAMENeurIPS 2022 D&B

MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge

Combines Minecraft tasks with a multimodal knowledge collection of videos, tutorials, wiki pages, and discussions for open-ended agent learning.

DatasetsLanguage & action
Details & sources

Authors: Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, Anima Anandkumar

Project name: MineDojo

Bibliographic source: Publisher ↗

Publisher record date: 2022-12-06

Credit: MineDojo research team

BibTeX citation
@misc{minedojo,
  title = {MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge},
  author = {Linxi Fan and Guanzhi Wang and Yunfan Jiang and Ajay Mandlekar and Yuncong Yang and Haoyi Zhu and Andrew Tang and De-An Huang and Yuke Zhu and Anima Anandkumar},
  year = {2022},
  url = {https://proceedings.neurips.cc/paper_files/paper/2022/hash/74a67268c5cc5910f64938cac4526a90-Abstract.html},
  doi = {10.52202/068431-1333}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Open X-Embodiment: Robotic Learning Datasets and RT-X Models
EMBODIEDICRA 2024

Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Brings together diverse robot trajectories in shared formats to study cross-robot transfer and generalist policies.

DatasetsVision-language-action
Details & sources

Authors: Embodiment Collaboration, Abby O'Neill, Abdul Rehman, Abhinav Gupta, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alex Herzog, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew Wang, Andrey Kolobov, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Beomjoon Kim, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu, Charlotte Le, Chelsea Finn, Chen Wang, Chenfeng Xu, Cheng Chi, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Driess, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Foster, Fangchen Liu, Federico Ceola, Fei Xia, Feiyu Zhao, Felipe Vieira Frujeri, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Ge Yan, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guangwen Yang, Guanzhi Wang, Hao Su, Hao-Shu Fang, Haochen Shi, Henghui Bao, Heni Ben Amor, Henrik I Christensen, Hiroki Furuta, Homanga Bharadhwaj, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim, Jaimyn Drake, Jan Peters, Jan Schneider, Jasmine Hsu, Jay Vakil, Jeannette Bohg, Jeffrey Bingham, Jeffrey Wu, Jensen Gao, Jiaheng Hu, Jiajun Wu, Jialin Wu, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jingyun Yang, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kaiyuan Wang, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Ken Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Lin, Kevin Zhang, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Lawrence Yunliang Chen, Lerrel Pinto, Li Fei-Fei, Liam Tan, Linxi "Jim" Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma, Moo Jin Kim, Muhammad Zubair Irshad, Naoaki Kanazawa, Nicklas Hansen, Nicolas Heess, Nikhil J Joshi, Niko Suenderhauf, Ning Liu, Norman Di Palo, Nur Muhammad Mahi Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R Sanketi, Patrick "Tree" Miller, Patrick Yin, Paul Wohlhart, Peng Xu, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Quan Vuong, Rafael Rafailov, Ran Tian, Ria Doshi, Roberto Martín-Martín, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Ruohan Zhang, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante, Sean Kirmani, Sergey Levine, Shan Lin, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham Sonawani, Shubham Tulsiani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vikash Kumar, Vincent Vanhoucke, Vitor Guizilini, Wei Zhan, Wenxuan Zhou, Wolfram Burgard, Xi Chen, Xiangyu Chen, Xiaolong Wang, Xinghao Zhu, Xinyang Geng, Xiyuan Liu, Xu Liangwei, Xuanlin Li, Yansong Pang, Yao Lu, Yecheng Jason Ma, Yejin Kim, Yevgen Chebotar, Yifan Zhou, Yifeng Zhu, Yilin Wu, Ying Xu, Yixuan Wang, Yonatan Bisk, Yongqiang Dou, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yue Cao, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zhuo Xu, Zichen Jeff Cui, Zichen Zhang, Zipeng Fu, Zipeng Lin

Project name: Open X-Embodiment

Bibliographic source: arXiv ↗

First arXiv submission: 2023-10-13

Credit: Open X-Embodiment Collaboration

BibTeX citation
@misc{open-x-embodiment,
  title = {Open X-Embodiment: Robotic Learning Datasets and RT-X Models},
  author = {Embodiment Collaboration and Abby O'Neill and Abdul Rehman and Abhinav Gupta and Abhiram Maddukuri and Abhishek Gupta and Abhishek Padalkar and Abraham Lee and Acorn Pooley and Agrim Gupta and Ajay Mandlekar and Ajinkya Jain and Albert Tung and Alex Bewley and Alex Herzog and Alex Irpan and Alexander Khazatsky and Anant Rai and Anchit Gupta and Andrew Wang and Andrey Kolobov and Anikait Singh and Animesh Garg and Aniruddha Kembhavi and Annie Xie and Anthony Brohan and Antonin Raffin and Archit Sharma and Arefeh Yavary and Arhan Jain and Ashwin Balakrishna and Ayzaan Wahid and Ben Burgess-Limerick and Beomjoon Kim and Bernhard Schölkopf and Blake Wulfe and Brian Ichter and Cewu Lu and Charles Xu and Charlotte Le and Chelsea Finn and Chen Wang and Chenfeng Xu and Cheng Chi and Chenguang Huang and Christine Chan and Christopher Agia and Chuer Pan and Chuyuan Fu and Coline Devin and Danfei Xu and Daniel Morton and Danny Driess and Daphne Chen and Deepak Pathak and Dhruv Shah and Dieter Büchler and Dinesh Jayaraman and Dmitry Kalashnikov and Dorsa Sadigh and Edward Johns and Ethan Foster and Fangchen Liu and Federico Ceola and Fei Xia and Feiyu Zhao and Felipe Vieira Frujeri and Freek Stulp and Gaoyue Zhou and Gaurav S. Sukhatme and Gautam Salhotra and Ge Yan and Gilbert Feng and Giulio Schiavi and Glen Berseth and Gregory Kahn and Guangwen Yang and Guanzhi Wang and Hao Su and Hao-Shu Fang and Haochen Shi and Henghui Bao and Heni Ben Amor and Henrik I Christensen and Hiroki Furuta and Homanga Bharadhwaj and Homer Walke and Hongjie Fang and Huy Ha and Igor Mordatch and Ilija Radosavovic and Isabel Leal and Jacky Liang and Jad Abou-Chakra and Jaehyung Kim and Jaimyn Drake and Jan Peters and Jan Schneider and Jasmine Hsu and Jay Vakil and Jeannette Bohg and Jeffrey Bingham and Jeffrey Wu and Jensen Gao and Jiaheng Hu and Jiajun Wu and Jialin Wu and Jiankai Sun and Jianlan Luo and Jiayuan Gu and Jie Tan and Jihoon Oh and Jimmy Wu and Jingpei Lu and Jingyun Yang and Jitendra Malik and João Silvério and Joey Hejna and Jonathan Booher and Jonathan Tompson and Jonathan Yang and Jordi Salvador and Joseph J. Lim and Junhyek Han and Kaiyuan Wang and Kanishka Rao and Karl Pertsch and Karol Hausman and Keegan Go and Keerthana Gopalakrishnan and Ken Goldberg and Kendra Byrne and Kenneth Oslund and Kento Kawaharazuka and Kevin Black and Kevin Lin and Kevin Zhang and Kiana Ehsani and Kiran Lekkala and Kirsty Ellis and Krishan Rana and Krishnan Srinivasan and Kuan Fang and Kunal Pratap Singh and Kuo-Hao Zeng and Kyle Hatch and Kyle Hsu and Laurent Itti and Lawrence Yunliang Chen and Lerrel Pinto and Li Fei-Fei and Liam Tan and Linxi "Jim" Fan and Lionel Ott and Lisa Lee and Luca Weihs and Magnum Chen and Marion Lepert and Marius Memmel and Masayoshi Tomizuka and Masha Itkina and Mateo Guaman Castro and Max Spero and Maximilian Du and Michael Ahn and Michael C. Yip and Mingtong Zhang and Mingyu Ding and Minho Heo and Mohan Kumar Srirama and Mohit Sharma and Moo Jin Kim and Muhammad Zubair Irshad and Naoaki Kanazawa and Nicklas Hansen and Nicolas Heess and Nikhil J Joshi and Niko Suenderhauf and Ning Liu and Norman Di Palo and Nur Muhammad Mahi Shafiullah and Oier Mees and Oliver Kroemer and Osbert Bastani and Pannag R Sanketi and Patrick "Tree" Miller and Patrick Yin and Paul Wohlhart and Peng Xu and Peter David Fagan and Peter Mitrano and Pierre Sermanet and Pieter Abbeel and Priya Sundaresan and Qiuyu Chen and Quan Vuong and Rafael Rafailov and Ran Tian and Ria Doshi and Roberto Martín-Martín and Rohan Baijal and Rosario Scalise and Rose Hendrix and Roy Lin and Runjia Qian and Ruohan Zhang and Russell Mendonca and Rutav Shah and Ryan Hoque and Ryan Julian and Samuel Bustamante and Sean Kirmani and Sergey Levine and Shan Lin and Sherry Moore and Shikhar Bahl and Shivin Dass and Shubham Sonawani and Shubham Tulsiani and Shuran Song and Sichun Xu and Siddhant Haldar and Siddharth Karamcheti and Simeon Adebola and Simon Guist and Soroush Nasiriany and Stefan Schaal and Stefan Welker and Stephen Tian and Subramanian Ramamoorthy and Sudeep Dasari and Suneel Belkhale and Sungjae Park and Suraj Nair and Suvir Mirchandani and Takayuki Osa and Tanmay Gupta and Tatsuya Harada and Tatsuya Matsushima and Ted Xiao and Thomas Kollar and Tianhe Yu and Tianli Ding and Todor Davchev and Tony Z. Zhao and Travis Armstrong and Trevor Darrell and Trinity Chung and Vidhi Jain and Vikash Kumar and Vincent Vanhoucke and Vitor Guizilini and Wei Zhan and Wenxuan Zhou and Wolfram Burgard and Xi Chen and Xiangyu Chen and Xiaolong Wang and Xinghao Zhu and Xinyang Geng and Xiyuan Liu and Xu Liangwei and Xuanlin Li and Yansong Pang and Yao Lu and Yecheng Jason Ma and Yejin Kim and Yevgen Chebotar and Yifan Zhou and Yifeng Zhu and Yilin Wu and Ying Xu and Yixuan Wang and Yonatan Bisk and Yongqiang Dou and Yoonyoung Cho and Youngwoon Lee and Yuchen Cui and Yue Cao and Yueh-Hua Wu and Yujin Tang and Yuke Zhu and Yunchu Zhang and Yunfan Jiang and Yunshuang Li and Yunzhu Li and Yusuke Iwasawa and Yutaka Matsuo and Zehan Ma and Zhuo Xu and Zichen Jeff Cui and Zichen Zhang and Zipeng Fu and Zipeng Lin},
  year = {2023},
  url = {https://doi.org/10.1109/ICRA57147.2024.10611477},
  doi = {10.1109/icra57147.2024.10611477}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of BridgeData V2: A Dataset for Robot Learning at Scale
EMBODIEDCoRL 2023

BridgeData V2: A Dataset for Robot Learning at Scale

Provides diverse low-cost robot manipulation trajectories across multiple environments for scalable imitation learning and offline reinforcement learning.

DatasetsImitation learning
Details & sources

Authors: Homer Rich Walke, Kevin Black, Tony Z. Zhao, Quan Vuong, Chongyi Zheng, Philippe Hansen-Estruch, Andre Wang He, Vivek Myers, Moo Jin Kim, Max Du, Abraham Lee, Kuan Fang, Chelsea Finn, Sergey Levine

Project name: BridgeData V2

Bibliographic source: Publisher ↗

Publisher record date: 2023-12-02

Credit: Homer Rich Walke and collaborators

BibTeX citation
@misc{bridgedata-v2,
  title = {BridgeData V2: A Dataset for Robot Learning at Scale},
  author = {Homer Rich Walke and Kevin Black and Tony Z. Zhao and Quan Vuong and Chongyi Zheng and Philippe Hansen-Estruch and Andre Wang He and Vivek Myers and Moo Jin Kim and Max Du and Abraham Lee and Kuan Fang and Chelsea Finn and Sergey Levine},
  year = {2023},
  url = {https://proceedings.mlr.press/v229/walke23a.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
EMBODIEDRSS 2024

DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

Collects manipulation demonstrations across many scenes and institutions to study learning from diverse real-world experience.

DatasetsImitation learning
Details & sources

Authors: Alexander Khazatsky, Karl Pertsch, Suraj Nair, Ashwin Balakrishna, Sudeep Dasari, Siddharth Karamcheti, Soroush Nasiriany, Mohan Kumar Srirama, Lawrence Yunliang Chen, Kirsty Ellis, Peter David Fagan, Joey Hejna, Masha Itkina, Marion Lepert, Yecheng Jason Ma, Patrick Tree Miller, Jimmy Wu, Suneel Belkhale, Shivin Dass, Huy Ha, Arhan Jain, Abraham Lee, Youngwoon Lee, Marius Memmel, Sungjae Park, Ilija Radosavovic, Kaiyuan Wang, Albert Zhan, Kevin Black, Cheng Chi, Kyle Beltran Hatch, Shan Lin, Jingpei Lu, Jean Mercat, Abdul Rehman, Pannag R Sanketi, Archit Sharma, Cody Simpson, Quan Vuong, Homer Rich Walke, Blake Wulfe, Ted Xiao, Jonathan Heewon Yang, Arefeh Yavary, Tony Z. Zhao, Christopher Agia, Rohan Baijal, Mateo Guaman Castro, Daphne Chen, Qiuyu Chen, Trinity Chung, Jaimyn Drake, Ethan Paul Foster, Jensen Gao, David Antonio Herrera, Minho Heo, Kyle Hsu, Jiaheng Hu, Donovon Jackson, Charlotte Le, Yunshuang Li, Roy Lin, Zehan Ma, Abhiram Maddukuri, Suvir Mirchandani, Daniel Morton, Tony Nguyen, Abigail O'Neill, Rosario Scalise, Derick Seale, Victor Son, Stephen Tian, Emi Tran, Andrew E. Wang, Yilin Wu, Annie Xie, Jingyun Yang, Patrick Yin, Yunchu Zhang, Osbert Bastani, Glen Berseth, Jeannette Bohg, Ken Goldberg, Abhinav Gupta, Abhishek Gupta, Dinesh Jayaraman, Joseph J Lim, Jitendra Malik, Roberto Martín-Martín, Subramanian Ramamoorthy, Dorsa Sadigh, Shuran Song, Jiajun Wu, Michael C. Yip, Yuke Zhu, Thomas Kollar, Sergey Levine, Chelsea Finn

Project name: DROID

Bibliographic source: Publisher ↗

Publisher record date: 2024-07-15

Credit: Alexander Khazatsky and the DROID collaboration

BibTeX citation
@misc{droid,
  title = {DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset},
  author = {Alexander Khazatsky and Karl Pertsch and Suraj Nair and Ashwin Balakrishna and Sudeep Dasari and Siddharth Karamcheti and Soroush Nasiriany and Mohan Kumar Srirama and Lawrence Yunliang Chen and Kirsty Ellis and Peter David Fagan and Joey Hejna and Masha Itkina and Marion Lepert and Yecheng Jason Ma and Patrick Tree Miller and Jimmy Wu and Suneel Belkhale and Shivin Dass and Huy Ha and Arhan Jain and Abraham Lee and Youngwoon Lee and Marius Memmel and Sungjae Park and Ilija Radosavovic and Kaiyuan Wang and Albert Zhan and Kevin Black and Cheng Chi and Kyle Beltran Hatch and Shan Lin and Jingpei Lu and Jean Mercat and Abdul Rehman and Pannag R Sanketi and Archit Sharma and Cody Simpson and Quan Vuong and Homer Rich Walke and Blake Wulfe and Ted Xiao and Jonathan Heewon Yang and Arefeh Yavary and Tony Z. Zhao and Christopher Agia and Rohan Baijal and Mateo Guaman Castro and Daphne Chen and Qiuyu Chen and Trinity Chung and Jaimyn Drake and Ethan Paul Foster and Jensen Gao and David Antonio Herrera and Minho Heo and Kyle Hsu and Jiaheng Hu and Donovon Jackson and Charlotte Le and Yunshuang Li and Roy Lin and Zehan Ma and Abhiram Maddukuri and Suvir Mirchandani and Daniel Morton and Tony Nguyen and Abigail O'Neill and Rosario Scalise and Derick Seale and Victor Son and Stephen Tian and Emi Tran and Andrew E. Wang and Yilin Wu and Annie Xie and Jingyun Yang and Patrick Yin and Yunchu Zhang and Osbert Bastani and Glen Berseth and Jeannette Bohg and Ken Goldberg and Abhinav Gupta and Abhishek Gupta and Dinesh Jayaraman and Joseph J Lim and Jitendra Malik and Roberto Martín-Martín and Subramanian Ramamoorthy and Dorsa Sadigh and Shuran Song and Jiajun Wu and Michael C. Yip and Yuke Zhu and Thomas Kollar and Sergey Levine and Chelsea Finn},
  year = {2024},
  url = {https://www.roboticsproceedings.org/rss20/p120.html}
}

Sources checked 2026-09-14

Added 2026-09-14

Open-source projects5Explore open-source projects

EMBODIEDTOOLRobot learning toolkit

LeRobot

Robot learning: datasets, policies and hardware

Integrates robot data collection, dataset formats, policy training, and hardware interfaces for experiments from demonstration to deployment.

Robot learningImitation learning
Details & evidence

Environment & tasks: Real robots and robot-learning datasets.

Observations & actions: Python robot interfaces, training utilities, and data tools.

Scope & limitations: Install model-specific drivers and calibrate the hardware. Check dataset, model, and code licenses separately.

Why included: Standalone open-source tool or environment supporting research and reproduction; excluded from paper-window statistics.

Evidence status: Official repository purpose and interfaces checked; not installed locally.

Checked 2026-09-14

Added 2026-09-14

EMBODIEDTOOLPolicy training toolkit

OpenPI

Physical Intelligence open robot policy tooling

Provides training, inference, and adaptation code for Physical Intelligence robot policies, connecting models to task data.

Vision-language-actionRobot learning
Details & evidence

Environment & tasks: Robot policy training and inference.

Observations & actions: Python, model serving, and dataset configuration.

Scope & limitations: Prepare model weights and hardware dependencies for the chosen release. Do not assume every component of a related paper has been released.

Why included: Standalone open-source tool or environment supporting research and reproduction; excluded from paper-window statistics.

Evidence status: Official repository purpose and interfaces checked; not installed locally.

Checked 2026-09-14

Added 2026-09-14 · Revised 2026-09-15

GAME + EMBODIEDTOOLEnvironment API

Gymnasium

A standard API for reinforcement learning environments

Provides a common environment interface for single-agent reinforcement learning experiments.

Reinforcement learningBenchmarks
Details & evidence

Environment & tasks: Diverse single-agent environments.

Observations & actions: Python reset / step API.

Scope & limitations: A standardized interface does not automatically align task difficulty, observation access, or rewards across environments.

Why included: Standalone open-source tool or environment supporting research and reproduction; excluded from paper-window statistics.

Evidence status: Official repository purpose and interfaces checked; not installed locally.

Checked 2026-09-14

Added 2026-09-14

GAMETOOLMulti-agent API

PettingZoo

Multi-agent reinforcement learning APIs

Provides multi-agent environment interfaces and reference tasks with turn-based and parallel execution.

Multi-agent learningReinforcement learning
Details & evidence

Environment & tasks: Cooperative, competitive, and mixed tasks.

Observations & actions: AEC and Parallel APIs.

Scope & limitations: Specify communication mechanisms, observability, opponents, and team-reward settings.

Why included: Standalone open-source tool or environment supporting research and reproduction; excluded from paper-window statistics.

Evidence status: Official repository purpose and interfaces checked; not installed locally.

Checked 2026-09-14

Added 2026-09-14

EMBODIEDTOOLPhysics simulator

Genesis

General-purpose robotics simulation

Provides general-purpose simulation infrastructure for robotics and embodied learning experiments with physical interaction.

Robot learningEnvironment generation
Details & evidence

Environment & tasks: Robot simulation scenes.

Observations & actions: Simulation and control interfaces.

Scope & limitations: Physics, hardware compatibility, and feature support depend on the release. Simulation does not establish validated sim-to-real transfer.

Why included: Standalone open-source tool or environment supporting research and reproduction; excluded from paper-window statistics.

Evidence status: Official repository purpose and interfaces checked; not installed locally.

Checked 2026-09-14

Added 2026-09-14

Datasets4Explore datasets

First page of MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge
GAMENeurIPS 2022 D&B

MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge

Combines Minecraft tasks with a multimodal knowledge collection of videos, tutorials, wiki pages, and discussions for open-ended agent learning.

DatasetsLanguage & action
Details & sources

Authors: Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, Anima Anandkumar

Project name: MineDojo

Bibliographic source: Publisher ↗

Publisher record date: 2022-12-06

Credit: MineDojo research team

BibTeX citation
@misc{minedojo,
  title = {MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge},
  author = {Linxi Fan and Guanzhi Wang and Yunfan Jiang and Ajay Mandlekar and Yuncong Yang and Haoyi Zhu and Andrew Tang and De-An Huang and Yuke Zhu and Anima Anandkumar},
  year = {2022},
  url = {https://proceedings.neurips.cc/paper_files/paper/2022/hash/74a67268c5cc5910f64938cac4526a90-Abstract.html},
  doi = {10.52202/068431-1333}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of Open X-Embodiment: Robotic Learning Datasets and RT-X Models
EMBODIEDICRA 2024

Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Brings together diverse robot trajectories in shared formats to study cross-robot transfer and generalist policies.

DatasetsVision-language-action
Details & sources

Authors: Embodiment Collaboration, Abby O'Neill, Abdul Rehman, Abhinav Gupta, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alex Herzog, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew Wang, Andrey Kolobov, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Beomjoon Kim, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu, Charlotte Le, Chelsea Finn, Chen Wang, Chenfeng Xu, Cheng Chi, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Driess, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Foster, Fangchen Liu, Federico Ceola, Fei Xia, Feiyu Zhao, Felipe Vieira Frujeri, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Ge Yan, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guangwen Yang, Guanzhi Wang, Hao Su, Hao-Shu Fang, Haochen Shi, Henghui Bao, Heni Ben Amor, Henrik I Christensen, Hiroki Furuta, Homanga Bharadhwaj, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim, Jaimyn Drake, Jan Peters, Jan Schneider, Jasmine Hsu, Jay Vakil, Jeannette Bohg, Jeffrey Bingham, Jeffrey Wu, Jensen Gao, Jiaheng Hu, Jiajun Wu, Jialin Wu, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jingyun Yang, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kaiyuan Wang, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Ken Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Lin, Kevin Zhang, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Lawrence Yunliang Chen, Lerrel Pinto, Li Fei-Fei, Liam Tan, Linxi "Jim" Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma, Moo Jin Kim, Muhammad Zubair Irshad, Naoaki Kanazawa, Nicklas Hansen, Nicolas Heess, Nikhil J Joshi, Niko Suenderhauf, Ning Liu, Norman Di Palo, Nur Muhammad Mahi Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R Sanketi, Patrick "Tree" Miller, Patrick Yin, Paul Wohlhart, Peng Xu, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Quan Vuong, Rafael Rafailov, Ran Tian, Ria Doshi, Roberto Martín-Martín, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Ruohan Zhang, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante, Sean Kirmani, Sergey Levine, Shan Lin, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham Sonawani, Shubham Tulsiani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vikash Kumar, Vincent Vanhoucke, Vitor Guizilini, Wei Zhan, Wenxuan Zhou, Wolfram Burgard, Xi Chen, Xiangyu Chen, Xiaolong Wang, Xinghao Zhu, Xinyang Geng, Xiyuan Liu, Xu Liangwei, Xuanlin Li, Yansong Pang, Yao Lu, Yecheng Jason Ma, Yejin Kim, Yevgen Chebotar, Yifan Zhou, Yifeng Zhu, Yilin Wu, Ying Xu, Yixuan Wang, Yonatan Bisk, Yongqiang Dou, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yue Cao, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zhuo Xu, Zichen Jeff Cui, Zichen Zhang, Zipeng Fu, Zipeng Lin

Project name: Open X-Embodiment

Bibliographic source: arXiv ↗

First arXiv submission: 2023-10-13

Credit: Open X-Embodiment Collaboration

BibTeX citation
@misc{open-x-embodiment,
  title = {Open X-Embodiment: Robotic Learning Datasets and RT-X Models},
  author = {Embodiment Collaboration and Abby O'Neill and Abdul Rehman and Abhinav Gupta and Abhiram Maddukuri and Abhishek Gupta and Abhishek Padalkar and Abraham Lee and Acorn Pooley and Agrim Gupta and Ajay Mandlekar and Ajinkya Jain and Albert Tung and Alex Bewley and Alex Herzog and Alex Irpan and Alexander Khazatsky and Anant Rai and Anchit Gupta and Andrew Wang and Andrey Kolobov and Anikait Singh and Animesh Garg and Aniruddha Kembhavi and Annie Xie and Anthony Brohan and Antonin Raffin and Archit Sharma and Arefeh Yavary and Arhan Jain and Ashwin Balakrishna and Ayzaan Wahid and Ben Burgess-Limerick and Beomjoon Kim and Bernhard Schölkopf and Blake Wulfe and Brian Ichter and Cewu Lu and Charles Xu and Charlotte Le and Chelsea Finn and Chen Wang and Chenfeng Xu and Cheng Chi and Chenguang Huang and Christine Chan and Christopher Agia and Chuer Pan and Chuyuan Fu and Coline Devin and Danfei Xu and Daniel Morton and Danny Driess and Daphne Chen and Deepak Pathak and Dhruv Shah and Dieter Büchler and Dinesh Jayaraman and Dmitry Kalashnikov and Dorsa Sadigh and Edward Johns and Ethan Foster and Fangchen Liu and Federico Ceola and Fei Xia and Feiyu Zhao and Felipe Vieira Frujeri and Freek Stulp and Gaoyue Zhou and Gaurav S. Sukhatme and Gautam Salhotra and Ge Yan and Gilbert Feng and Giulio Schiavi and Glen Berseth and Gregory Kahn and Guangwen Yang and Guanzhi Wang and Hao Su and Hao-Shu Fang and Haochen Shi and Henghui Bao and Heni Ben Amor and Henrik I Christensen and Hiroki Furuta and Homanga Bharadhwaj and Homer Walke and Hongjie Fang and Huy Ha and Igor Mordatch and Ilija Radosavovic and Isabel Leal and Jacky Liang and Jad Abou-Chakra and Jaehyung Kim and Jaimyn Drake and Jan Peters and Jan Schneider and Jasmine Hsu and Jay Vakil and Jeannette Bohg and Jeffrey Bingham and Jeffrey Wu and Jensen Gao and Jiaheng Hu and Jiajun Wu and Jialin Wu and Jiankai Sun and Jianlan Luo and Jiayuan Gu and Jie Tan and Jihoon Oh and Jimmy Wu and Jingpei Lu and Jingyun Yang and Jitendra Malik and João Silvério and Joey Hejna and Jonathan Booher and Jonathan Tompson and Jonathan Yang and Jordi Salvador and Joseph J. Lim and Junhyek Han and Kaiyuan Wang and Kanishka Rao and Karl Pertsch and Karol Hausman and Keegan Go and Keerthana Gopalakrishnan and Ken Goldberg and Kendra Byrne and Kenneth Oslund and Kento Kawaharazuka and Kevin Black and Kevin Lin and Kevin Zhang and Kiana Ehsani and Kiran Lekkala and Kirsty Ellis and Krishan Rana and Krishnan Srinivasan and Kuan Fang and Kunal Pratap Singh and Kuo-Hao Zeng and Kyle Hatch and Kyle Hsu and Laurent Itti and Lawrence Yunliang Chen and Lerrel Pinto and Li Fei-Fei and Liam Tan and Linxi "Jim" Fan and Lionel Ott and Lisa Lee and Luca Weihs and Magnum Chen and Marion Lepert and Marius Memmel and Masayoshi Tomizuka and Masha Itkina and Mateo Guaman Castro and Max Spero and Maximilian Du and Michael Ahn and Michael C. Yip and Mingtong Zhang and Mingyu Ding and Minho Heo and Mohan Kumar Srirama and Mohit Sharma and Moo Jin Kim and Muhammad Zubair Irshad and Naoaki Kanazawa and Nicklas Hansen and Nicolas Heess and Nikhil J Joshi and Niko Suenderhauf and Ning Liu and Norman Di Palo and Nur Muhammad Mahi Shafiullah and Oier Mees and Oliver Kroemer and Osbert Bastani and Pannag R Sanketi and Patrick "Tree" Miller and Patrick Yin and Paul Wohlhart and Peng Xu and Peter David Fagan and Peter Mitrano and Pierre Sermanet and Pieter Abbeel and Priya Sundaresan and Qiuyu Chen and Quan Vuong and Rafael Rafailov and Ran Tian and Ria Doshi and Roberto Martín-Martín and Rohan Baijal and Rosario Scalise and Rose Hendrix and Roy Lin and Runjia Qian and Ruohan Zhang and Russell Mendonca and Rutav Shah and Ryan Hoque and Ryan Julian and Samuel Bustamante and Sean Kirmani and Sergey Levine and Shan Lin and Sherry Moore and Shikhar Bahl and Shivin Dass and Shubham Sonawani and Shubham Tulsiani and Shuran Song and Sichun Xu and Siddhant Haldar and Siddharth Karamcheti and Simeon Adebola and Simon Guist and Soroush Nasiriany and Stefan Schaal and Stefan Welker and Stephen Tian and Subramanian Ramamoorthy and Sudeep Dasari and Suneel Belkhale and Sungjae Park and Suraj Nair and Suvir Mirchandani and Takayuki Osa and Tanmay Gupta and Tatsuya Harada and Tatsuya Matsushima and Ted Xiao and Thomas Kollar and Tianhe Yu and Tianli Ding and Todor Davchev and Tony Z. Zhao and Travis Armstrong and Trevor Darrell and Trinity Chung and Vidhi Jain and Vikash Kumar and Vincent Vanhoucke and Vitor Guizilini and Wei Zhan and Wenxuan Zhou and Wolfram Burgard and Xi Chen and Xiangyu Chen and Xiaolong Wang and Xinghao Zhu and Xinyang Geng and Xiyuan Liu and Xu Liangwei and Xuanlin Li and Yansong Pang and Yao Lu and Yecheng Jason Ma and Yejin Kim and Yevgen Chebotar and Yifan Zhou and Yifeng Zhu and Yilin Wu and Ying Xu and Yixuan Wang and Yonatan Bisk and Yongqiang Dou and Yoonyoung Cho and Youngwoon Lee and Yuchen Cui and Yue Cao and Yueh-Hua Wu and Yujin Tang and Yuke Zhu and Yunchu Zhang and Yunfan Jiang and Yunshuang Li and Yunzhu Li and Yusuke Iwasawa and Yutaka Matsuo and Zehan Ma and Zhuo Xu and Zichen Jeff Cui and Zichen Zhang and Zipeng Fu and Zipeng Lin},
  year = {2023},
  url = {https://doi.org/10.1109/ICRA57147.2024.10611477},
  doi = {10.1109/icra57147.2024.10611477}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of BridgeData V2: A Dataset for Robot Learning at Scale
EMBODIEDCoRL 2023

BridgeData V2: A Dataset for Robot Learning at Scale

Provides diverse low-cost robot manipulation trajectories across multiple environments for scalable imitation learning and offline reinforcement learning.

DatasetsImitation learning
Details & sources

Authors: Homer Rich Walke, Kevin Black, Tony Z. Zhao, Quan Vuong, Chongyi Zheng, Philippe Hansen-Estruch, Andre Wang He, Vivek Myers, Moo Jin Kim, Max Du, Abraham Lee, Kuan Fang, Chelsea Finn, Sergey Levine

Project name: BridgeData V2

Bibliographic source: Publisher ↗

Publisher record date: 2023-12-02

Credit: Homer Rich Walke and collaborators

BibTeX citation
@misc{bridgedata-v2,
  title = {BridgeData V2: A Dataset for Robot Learning at Scale},
  author = {Homer Rich Walke and Kevin Black and Tony Z. Zhao and Quan Vuong and Chongyi Zheng and Philippe Hansen-Estruch and Andre Wang He and Vivek Myers and Moo Jin Kim and Max Du and Abraham Lee and Kuan Fang and Chelsea Finn and Sergey Levine},
  year = {2023},
  url = {https://proceedings.mlr.press/v229/walke23a.html}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
EMBODIEDRSS 2024

DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

Collects manipulation demonstrations across many scenes and institutions to study learning from diverse real-world experience.

DatasetsImitation learning
Details & sources

Authors: Alexander Khazatsky, Karl Pertsch, Suraj Nair, Ashwin Balakrishna, Sudeep Dasari, Siddharth Karamcheti, Soroush Nasiriany, Mohan Kumar Srirama, Lawrence Yunliang Chen, Kirsty Ellis, Peter David Fagan, Joey Hejna, Masha Itkina, Marion Lepert, Yecheng Jason Ma, Patrick Tree Miller, Jimmy Wu, Suneel Belkhale, Shivin Dass, Huy Ha, Arhan Jain, Abraham Lee, Youngwoon Lee, Marius Memmel, Sungjae Park, Ilija Radosavovic, Kaiyuan Wang, Albert Zhan, Kevin Black, Cheng Chi, Kyle Beltran Hatch, Shan Lin, Jingpei Lu, Jean Mercat, Abdul Rehman, Pannag R Sanketi, Archit Sharma, Cody Simpson, Quan Vuong, Homer Rich Walke, Blake Wulfe, Ted Xiao, Jonathan Heewon Yang, Arefeh Yavary, Tony Z. Zhao, Christopher Agia, Rohan Baijal, Mateo Guaman Castro, Daphne Chen, Qiuyu Chen, Trinity Chung, Jaimyn Drake, Ethan Paul Foster, Jensen Gao, David Antonio Herrera, Minho Heo, Kyle Hsu, Jiaheng Hu, Donovon Jackson, Charlotte Le, Yunshuang Li, Roy Lin, Zehan Ma, Abhiram Maddukuri, Suvir Mirchandani, Daniel Morton, Tony Nguyen, Abigail O'Neill, Rosario Scalise, Derick Seale, Victor Son, Stephen Tian, Emi Tran, Andrew E. Wang, Yilin Wu, Annie Xie, Jingyun Yang, Patrick Yin, Yunchu Zhang, Osbert Bastani, Glen Berseth, Jeannette Bohg, Ken Goldberg, Abhinav Gupta, Abhishek Gupta, Dinesh Jayaraman, Joseph J Lim, Jitendra Malik, Roberto Martín-Martín, Subramanian Ramamoorthy, Dorsa Sadigh, Shuran Song, Jiajun Wu, Michael C. Yip, Yuke Zhu, Thomas Kollar, Sergey Levine, Chelsea Finn

Project name: DROID

Bibliographic source: Publisher ↗

Publisher record date: 2024-07-15

Credit: Alexander Khazatsky and the DROID collaboration

BibTeX citation
@misc{droid,
  title = {DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset},
  author = {Alexander Khazatsky and Karl Pertsch and Suraj Nair and Ashwin Balakrishna and Sudeep Dasari and Siddharth Karamcheti and Soroush Nasiriany and Mohan Kumar Srirama and Lawrence Yunliang Chen and Kirsty Ellis and Peter David Fagan and Joey Hejna and Masha Itkina and Marion Lepert and Yecheng Jason Ma and Patrick Tree Miller and Jimmy Wu and Suneel Belkhale and Shivin Dass and Huy Ha and Arhan Jain and Abraham Lee and Youngwoon Lee and Marius Memmel and Sungjae Park and Ilija Radosavovic and Kaiyuan Wang and Albert Zhan and Kevin Black and Cheng Chi and Kyle Beltran Hatch and Shan Lin and Jingpei Lu and Jean Mercat and Abdul Rehman and Pannag R Sanketi and Archit Sharma and Cody Simpson and Quan Vuong and Homer Rich Walke and Blake Wulfe and Ted Xiao and Jonathan Heewon Yang and Arefeh Yavary and Tony Z. Zhao and Christopher Agia and Rohan Baijal and Mateo Guaman Castro and Daphne Chen and Qiuyu Chen and Trinity Chung and Jaimyn Drake and Ethan Paul Foster and Jensen Gao and David Antonio Herrera and Minho Heo and Kyle Hsu and Jiaheng Hu and Donovon Jackson and Charlotte Le and Yunshuang Li and Roy Lin and Zehan Ma and Abhiram Maddukuri and Suvir Mirchandani and Daniel Morton and Tony Nguyen and Abigail O'Neill and Rosario Scalise and Derick Seale and Victor Son and Stephen Tian and Emi Tran and Andrew E. Wang and Yilin Wu and Annie Xie and Jingyun Yang and Patrick Yin and Yunchu Zhang and Osbert Bastani and Glen Berseth and Jeannette Bohg and Ken Goldberg and Abhinav Gupta and Abhishek Gupta and Dinesh Jayaraman and Joseph J Lim and Jitendra Malik and Roberto Martín-Martín and Subramanian Ramamoorthy and Dorsa Sadigh and Shuran Song and Jiajun Wu and Michael C. Yip and Yuke Zhu and Thomas Kollar and Sergey Levine and Chelsea Finn},
  year = {2024},
  url = {https://www.roboticsproceedings.org/rss20/p120.html}
}

Sources checked 2026-09-14

Added 2026-09-14

Benchmarks & environments8Explore benchmarks & environments

First page of COOM: A Game Benchmark for Continual Reinforcement Learning
GAMENeurIPS 2023 Datasets and Benchmarks

COOM: A Game Benchmark for Continual Reinforcement Learning

Evaluates continual reinforcement learning under sequential visual and task shifts, tracking forgetting, forward transfer, and new-task learning.

Continual learningBenchmarks
Details & sources

Authors: Tristan Tomilin, Meng Fang, Yudi Zhang, Mykola Pechenizkiy

Project name: COOM

Bibliographic source: Publisher ↗

Publisher record date: 2023-12-15

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-b01,
  title = {COOM: A Game Benchmark for Continual Reinforcement Learning},
  author = {Tristan Tomilin and Meng Fang and Yudi Zhang and Mykola Pechenizkiy},
  year = {2023},
  url = {https://proceedings.neurips.cc/paper_files/paper/2023/hash/d61d9f4fe4357296cb658795fd7999f0-Abstract-Datasets_and_Benchmarks.html},
  doi = {10.52202/075280-2965}
}

Sources checked 2026-09-14 · Knowledge-base ID B01

Added 2026-09-14

First page of HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents
GAMEICLR 2025

HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents

Separates task rewards from violation costs to evaluate visual agents under hazards such as dangerous terrain and friendly fire.

Safety & verificationBenchmarks
Details & sources

Authors: Tristan Tomilin, Meng Fang, Mykola Pechenizkiy

Project name: HASARD

Bibliographic source: Publisher ↗

Publisher record date: 2025-05-01

Original knowledge-base review label: Compiled from original notes

BibTeX citation
@misc{kb-b02,
  title = {HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents},
  author = {Tristan Tomilin and Meng Fang and Mykola Pechenizkiy},
  year = {2025},
  url = {https://proceedings.iclr.cc/paper_files/paper/2025/hash/5ab848771ff8c9c47aac4128e2ef9f4e-Abstract-Conference.html}
}

Sources checked 2026-09-14 · Knowledge-base ID B02

Added 2026-09-14

First page of LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
EMBODIEDNeurIPS 2023 D&B

LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning

Evaluates lifelong robot learning and knowledge transfer across changes in spatial relations, objects, goals, and task combinations.

BenchmarksContinual learning
Details & sources

Authors: Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, Peter Stone

Project name: LIBERO

Bibliographic source: Publisher ↗

Publisher record date: 2023-12-15

BibTeX citation
@misc{libero,
  title = {LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning},
  author = {Bo Liu and Yifeng Zhu and Chongkai Gao and Yihao Feng and Qiang Liu and Yuke Zhu and Peter Stone},
  year = {2023},
  url = {https://proceedings.neurips.cc/paper_files/paper/2023/hash/8c3c666820ea055a77726d66fc7d447f-Abstract-Datasets_and_Benchmarks.html},
  doi = {10.52202/075280-1939}
}

Sources checked 2026-09-14

Added 2026-09-14

First page of EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents
EMBODIEDICML 2025

EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents

Systematically evaluates multimodal language models on decision-making in visually grounded embodied tasks.

BenchmarksLanguage & action
Details & sources

Authors: Rui Yang, Hanyang Chen, Junyu Zhang, Mark Zhao, Cheng Qian, Kangrui Wang, Qineng Wang, Teja Venkat Koripella, Marziyeh Movahedi, Manling Li, Heng Ji, Huan Zhang, Tong Zhang

Project name: EmbodiedBench

Bibliographic source: Publisher ↗

Publisher record date: 2025-10-06

BibTeX citation
@misc{embodiedbench,
  title = {EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents},
  author = {Rui Yang and Hanyang Chen and Junyu Zhang and Mark Zhao and Cheng Qian and Kangrui Wang and Qineng Wang and Teja Venkat Koripella and Marziyeh Movahedi and Manling Li and Heng Ji and Huan Zhang and Tong Zhang},
  year = {2025},
  url = {https://proceedings.mlr.press/v267/yang25f.html}
}

Sources checked 2026-09-14

Added 2026-09-14

GAMETOOLGame research environment

ViZDoom

Doom-based visual reinforcement learning environments

Provides configurable first-person visual tasks based on DOOM for navigation, survival, and shooting research.

BenchmarksReinforcement learning
Details & evidence

Environment & tasks: DOOM.

Observations & actions: Visual observations, game variables, and discrete actions.

Scope & limitations: Specify access to game variables, frame skipping, and the action set. The underlying environment is not subject to the paper publication window.

Why included: Standalone open-source tool or environment supporting research and reproduction; excluded from paper-window statistics.

Evidence status: Official repository purpose and interfaces checked; not installed locally.

Checked 2026-09-14

Added 2026-09-14

EMBODIEDTOOLEmbodied research environment

Habitat-Lab

Embodied AI tasks and environments

Provides embodied tasks, environment configuration, and training/evaluation components for navigation and interaction research.

BenchmarksPlanning & memory
Details & evidence

Environment & tasks: 3D indoor environments.

Observations & actions: Sensor observations and task-action interfaces.

Scope & limitations: Scene datasets and the simulator must be configured together. Evaluation protocols depend on the task.

Why included: Standalone open-source tool or environment supporting research and reproduction; excluded from paper-window statistics.

Evidence status: Official repository purpose and interfaces checked; not installed locally.

Checked 2026-09-14

Added 2026-09-14

EMBODIEDTOOLInteractive environment

AI2-THOR

Interactive 3D environments for visual AI

Provides interactive indoor scenes and objects for visual navigation, object interaction, and embodied planning.

BenchmarksPlanning & memory
Details & evidence

Environment & tasks: Interactive 3D indoor scenes.

Observations & actions: Visual observations and environment action APIs.

Scope & limitations: Discrete object interaction differs from real joint-level control. State the exact task definition.

Why included: Standalone open-source tool or environment supporting research and reproduction; excluded from paper-window statistics.

Evidence status: Official repository purpose and interfaces checked; not installed locally.

Checked 2026-09-14

Added 2026-09-14

EMBODIEDTOOLManipulation benchmark

ManiSkill

GPU-parallelized robotics simulation and benchmarks

Provides parallel simulation, task environments, and evaluation tools for robot manipulation skills.

BenchmarksRobot learning
Details & evidence

Environment & tasks: Simulated robot manipulation.

Observations & actions: Simulated observations, actions, and skill tasks.

Scope & limitations: Specify the environment version, robot, control mode, and training data. Simulation throughput is not real-world control performance.

Why included: Standalone open-source tool or environment supporting research and reproduction; excluded from paper-window statistics.

Evidence status: Official repository purpose and interfaces checked; not installed locally.

Checked 2026-09-14

Added 2026-09-14

Video demos17Explore video demos

Game control2023

Voyager research team

Voyager · Minecraft mining

A mining-skill example from the official Voyager Minecraft gallery.

Details & evidence

Setting: Minecraft.

Interface: JavaScript skill code, game state, and execution feedback.

Playback & limitations: Programmatic skill execution with Mineflayer; the authors render demonstrations from recorded gameplay, which is not a record of pixel-only control.

Evidence: Original project video and source page checked. Qualitative, author-reported evidence; not independently reproduced.

Checked 2026-09-14. Year identifies the source project release, not the exact recording date.

Added 2026-09-14

Game control2023

Voyager research team

Voyager · Collecting cactus

A second official Voyager example showing resource collection.

Details & evidence

Setting: Minecraft.

Interface: JavaScript skill code, game state, and execution feedback.

Playback & limitations: A selected skill demonstration, not evidence of success on every task. The recording viewpoint is distinct from the programmatic observation interface.

Evidence: Original project video and source page checked. Qualitative, author-reported evidence; not independently reproduced.

Checked 2026-09-14. Year identifies the source project release, not the exact recording date.

Added 2026-09-14

Generated world2024

GameNGen research team

GameNGen · Neural DOOM

A DOOM sequence generated by an action-conditioned neural simulator.

Details & evidence

Setting: DOOM / ViZDoom.

Interface: Past game frames and player actions condition diffusion-based next-frame prediction. PPO primarily collects training trajectories.

Playback & limitations: This demonstrates frame generation conditioned on actions. It is not an autonomous game-playing policy or a measured combat result.

Evidence: Original project video and source page checked. Qualitative, author-reported evidence; not independently reproduced.

Checked 2026-09-14. Year identifies the source project release, not the exact recording date.

Added 2026-09-14

Generated world2024

DIAMOND research team

DIAMOND · Generated CS:GO

An interactive Counter-Strike sequence from the diffusion world-model project.

Details & evidence

Setting: Atari; Counter-Strike: Global Offensive, Dust II.

Interface: Human CS:GO frames and actions feed low-resolution dynamics and super-resolution generation. The shooter extension does not train an RL policy.

Playback & limitations: Keep the CS:GO generation extension separate from Atari policy-learning results. Visual continuity is not a combat success metric.

Evidence: Original project video and source page checked. Qualitative, author-reported evidence; not independently reproduced.

Checked 2026-09-14. Year identifies the source project release, not the exact recording date.

Added 2026-09-14

Real robot2024

OpenVLA research team

OpenVLA · Manipulation in clutter

A real WidowX manipulation rollout from the OpenVLA project gallery.

Details & evidence

Setting: Multiple real-world robot manipulation platforms.

Interface: RGB images and language map to robot actions.

Playback & limitations: The source clip is labeled 1.5× speed. This selected qualitative rollout is not a control-frequency measurement or an aggregate success rate.

Evidence: Original project video and source page checked. Qualitative, author-reported evidence; not independently reproduced.

Checked 2026-09-14. Year identifies the source project release, not the exact recording date.

Added 2026-09-14

Real robot2023

Tony Zhao and the ALOHA / ACT team

ACT / ALOHA · Slotting a battery

A learned ACT policy performs a precise bimanual battery-insertion task.

Details & evidence

Setting: The ALOHA bimanual platform.

Interface: Images and joint observations map to action chunks.

Playback & limitations: The source labels this a real-time learned-policy rollout, separate from its teleoperation examples. This selected task does not establish arbitrary dexterity.

Evidence: Original project video and source page checked. Qualitative, author-reported evidence; not independently reproduced.

Checked 2026-09-14. Year identifies the source project release, not the exact recording date.

Added 2026-09-14

Real robot2023

Cheng Chi and the Diffusion Policy team

Diffusion Policy · Mug manipulation

An official real-robot manipulation example using a diffusion action policy.

Details & evidence

Setting: Simulated and real-world robot manipulation tasks.

Interface: Visual observations map to continuous action sequences.

Playback & limitations: A selected physical execution example; the action distribution, demonstration data, and evaluation setup remain task-specific.

Evidence: Original project video and source page checked. Qualitative, author-reported evidence; not independently reproduced.

Checked 2026-09-14. Year identifies the source project release, not the exact recording date.

Added 2026-09-14

Simulation2023

Cheng Chi and the Diffusion Policy team

Diffusion Policy · Simulated lifting

A simulation rollout from the diffusion-policy evaluation gallery.

Details & evidence

Setting: Simulated and real-world robot manipulation tasks.

Interface: Visual observations map to continuous action sequences.

Playback & limitations: Simulation evidence is distinct from the real-robot videos. A short successful rollout is not an aggregate success rate.

Evidence: Original project video and source page checked. Qualitative, author-reported evidence; not independently reproduced.

Checked 2026-09-14. Year identifies the source project release, not the exact recording date.

Added 2026-09-14

Game control2022

Crafter research team

Crafter · open-world survival

An official Crafter sequence showing resource gathering, survival, and crafting in the compact open-world benchmark.

Details & evidence

Setting: Crafter procedural survival world.

Interface: Pixels and discrete movement, interaction, sleep, and crafting actions.

Playback & limitations: The clip illustrates a trajectory in a compact 2D benchmark; it is not a measure of average agent performance.

Evidence: Original project video and source page checked. Qualitative, author-reported evidence; not independently reproduced.

Checked 2026-09-14. Year identifies the source project release, not the exact recording date.

Added 2026-09-14

Simulation2023

VIMA research team

VIMA · one-shot video imitation

An official VIMA-Bench example in which a multimodal prompt demonstrates the motion the simulated robot should reproduce.

Details & evidence

Setting: VIMA-Bench tabletop simulation.

Interface: A one-shot video prompt and scene observations condition object-centric robot actions.

Playback & limitations: The clip uses a simulator and benchmark object representation; it does not demonstrate direct transfer to a physical robot.

Evidence: Original project video and source page checked. Qualitative, author-reported evidence; not independently reproduced.

Checked 2026-09-14. Year identifies the source project release, not the exact recording date.

Added 2026-09-14

Real robot2024

Mobile ALOHA research team

Mobile ALOHA · cooking shrimp

The official Mobile ALOHA gallery shows autonomous whole-body bimanual cooking and serving of shrimp.

Details & evidence

Setting: A real kitchen with a mobile dual-arm ALOHA platform.

Interface: Camera observations drive learned base, arm, and gripper actions cloned from whole-body demonstrations.

Playback & limitations: A selected successful rollout does not report the task success distribution or cover changes outside the evaluated kitchen setup.

Evidence: Original project video and source page checked. Qualitative, author-reported evidence; not independently reproduced.

Checked 2026-09-14. Year identifies the source project release, not the exact recording date.

Added 2026-09-14

Real robot2024

3D Diffusion Policy research team

DP3 · spatially robust pouring

An official real-robot DP3 sequence demonstrates point-cloud-conditioned pouring under spatial variation.

Details & evidence

Setting: Real tabletop pouring with sparse point-cloud observations.

Interface: Depth-derived point clouds and proprioception condition a diffusion policy over action chunks.

Playback & limitations: The gallery clip is qualitative evidence from the authors and does not expose the full evaluation distribution.

Evidence: Original project video and source page checked. Qualitative, author-reported evidence; not independently reproduced.

Checked 2026-09-14. Year identifies the source project release, not the exact recording date.

Added 2026-09-14

Real robot2024

GR-1 research team

GR-1 · opening a drawer

The official GR-1 page shows an end-to-end real-robot drawer-opening behavior after video-generative pretraining and policy fine-tuning.

Details & evidence

Setting: Real tabletop articulated-object manipulation.

Interface: Language, image history, and robot state tokens condition action and future-image predictions.

Playback & limitations: This is one author-selected rollout; it does not isolate how much performance comes from video pretraining versus robot fine-tuning.

Evidence: Original project video and source page checked. Qualitative, author-reported evidence; not independently reproduced.

Checked 2026-09-14. Year identifies the source project release, not the exact recording date.

Added 2026-09-14

Real robot2022

SayCan research team

SayCan · long-horizon instruction

An official SayCan demonstration shows language planning grounded in the feasible skills of a mobile manipulation robot.

Details & evidence

Setting: A real office kitchen with a mobile manipulator.

Interface: A natural-language request is decomposed into value-weighted skills and executed sequentially.

Playback & limitations: The planner selects from a fixed skill library; a successful sequence does not imply open-ended low-level control.

Evidence: Original project video and source page checked. Qualitative, author-reported evidence; not independently reproduced.

Checked 2026-09-14. Year identifies the source project release, not the exact recording date.

Added 2026-09-14

Real robot2024

3D Diffuser Actor research team

3D Diffuser Actor · peg insertion

The official gallery shows a 3D trajectory diffusion policy inserting a peg into a hole on a real robot.

Details & evidence

Setting: Real tabletop contact-rich manipulation.

Interface: RGB-D scene features, language, and proprioception condition a denoised end-effector trajectory.

Playback & limitations: A gallery success does not establish tolerance across unseen geometry, calibration error, or contact conditions.

Evidence: Original project video and source page checked. Qualitative, author-reported evidence; not independently reproduced.

Checked 2026-09-14. Year identifies the source project release, not the exact recording date.

Added 2026-09-14

Real robot2021

CLIPort research team

CLIPort · cloth folding

An official CLIPort real-robot clip shows language-conditioned cloth folding with semantic and spatial action pathways.

Details & evidence

Setting: Real tabletop cloth manipulation.

Interface: RGB observations and language condition pick-and-place predictions.

Playback & limitations: The action abstraction is tailored to tabletop pick-and-place and cannot represent every deformation or continuous force profile.

Evidence: Original project video and source page checked. Qualitative, author-reported evidence; not independently reproduced.

Checked 2026-09-14. Year identifies the source project release, not the exact recording date.

Added 2026-09-14

Real robot2022

Inner Monologue research team

Inner Monologue · feedback-driven recovery

An official example shows a language planner using scene and success feedback to update a multi-step manipulation plan.

Details & evidence

Setting: Real or simulated tabletop rearrangement from the official evaluation gallery.

Interface: Textual environment feedback is returned to the language model between skill executions.

Playback & limitations: Recovery depends on the accuracy of external feedback modules and the coverage of the available skill library.

Evidence: Original project video and source page checked. Qualitative, author-reported evidence; not independently reproduced.

Checked 2026-09-14. Year identifies the source project release, not the exact recording date.

Added 2026-09-14

Collection revised · 2026-09-15Metrics checked · 2026-10-19Updates & maintenance ↗