mlsanityData to 7 Oct 2026

Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

arXiv 2108.06038 ↗First posted 13 Aug 2021cs.RO cs.AIcited by 16

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Abstract (arXiv)

We present a method for learning a human-robot collaboration policy from human-human collaboration demonstrations. An effective robot assistant must learn to handle diverse human behaviors shown in the demonstrations and be robust when the humans adjust their strategies during online task execution. Our method co-optimizes a human policy and a robot policy in an interactive learning process: the human policy learns to generate diverse and plausible collaborative behaviors from demonstrations while the robot policy learns to assist by estimating the unobserved latent strategy of its human collaborator. Across a 2D strategy game, a human-robot handover task, and a multi-step collaborative manipulation task, our method outperforms the alternatives in both simulated evaluations and when executing the tasks with a real human operator in-the-loop. Supplementary materials and videos at https://sites.google.com/view/co-gail-web/home

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 34 of its 54 references that are in the map. First posted on arXiv 13 Aug 2021.

Builds on

34 of 54 references.

References · 34

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22 more
  1. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  2. 2017Multi-agent Reinforcement Learning in Sequential Social Dilemmascited by 206
  3. 2018RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitationcited by 202
  4. 2015Multiagent Cooperation and Competition with Deep Reinforcement Learningcited by 180
  5. 2015Variational Information Maximisation for Intrinsically Motivated Reinforcement Learningcited by 142
  6. 2018Variational Option Discovery Algorithmscited by 121
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  15. 2014Efficient Model Learning for Human-Robot Collaborative Taskscited by 35
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  17. 2020Reactive Human-to-Robot Handovers of Arbitrary Objectscited by 25
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 6; 2024: 4; 2025: 4; 2026: 1.

16 papers.

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  1. 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 112
  2. 2024COMBO: Compositional World Models for Embodied Multi-Agent Cooperationcited by 47
  3. 2023Human-in-the-Loop Task and Motion Planning for Imitation Learningcited by 28
  4. 2024Learning to Cooperate with Humans using Generative Agentscited by 23
  5. 2023Adaptive Coordination in Social Embodied Rearrangementcited by 14
  6. 2022Heterogeneous Multi-agent Zero-Shot Coordination by Coevolutioncited by 12
  7. 2023Automated Task-Time Interventions to Improve Teamwork using Imitation Learningcited by 7
  8. 2025RHINO: Learning Real-Time Humanoid-Human-Object Interaction from Human Demonstrationscited by 4
  9. 2024MoVEInt: Mixture of Variational Experts for Learning Human-Robot Interactions from Demonstrationscited by 3
  10. 2025Autonomous Human-Robot Interaction via Operator Imitationcited by 3
  11. 2025Adaptively Coordinating with Novel Partners via Learned Latent Strategiescited by 3
  12. 2023Multi-Agent Strategy Explanations for Human-Robot Collaborationcited by 2
4 more
  1. 2023Learning Multimodal Latent Dynamics for Human-Robot Interactioncited by 1
  2. 2025Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Modelscited by 1
  3. 2024Open Human-Robot Collaboration using Decentralized Inverse Reinforcement Learningcited by 0
  4. 2026OmniRobotHome: A Multi-Camera Home Platform for Real-Time Human-Robot Interactioncited by 0