mlsanityData to 14 Oct 2026

Open Human-Robot Collaboration using Decentralized Inverse Reinforcement Learning

arXiv 2410.01790 ↗First posted 2 Oct 2024cs.ROcited by 0

On the map: Offline RL and imitation learning from data → Inverse reinforcement learning

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Abstract (arXiv)

The growing interest in human-robot collaboration (HRC), where humans and robots cooperate towards shared goals, has seen significant advancements over the past decade. While previous research has addressed various challenges, several key issues remain unresolved. Many domains within HRC involve activities that do not necessarily require human presence throughout the entire task. Existing literature typically models HRC as a closed system, where all agents are present for the entire duration of the task. In contrast, an open model offers flexibility by allowing an agent to enter and exit the collaboration as needed, enabling them to concurrently manage other tasks. In this paper, we introduce a novel multiagent framework called oDec-MDP, designed specifically to model open HRC scenarios where agents can join or leave tasks flexibly during execution. We generalize a recent multiagent inverse reinforcement learning method - Dec-AIRL to learn from open systems modeled using the oDec-MDP. Our method is validated through experiments conducted in both a simplified toy firefighting domain and a realistic dyadic human-robot collaborative assembly. Results show that our framework and learning method improves upon its closed system counterpart.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 12 of its 28 references that are in the map. First posted on arXiv 2 Oct 2024.

Builds on

12 of 28 references.

References · 12

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2020MediaPipe Hands: On-device Real-time Hand Trackingcited by 161
  3. 2018Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planningcited by 32
  4. 2017Game-Theoretic Modeling of Human Adaptation in Human-Robot Collaborationcited by 21
  5. 2021Co-GAIL: Learning Diverse Strategies for Human-Robot Collaborationcited by 17
  6. 2022A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learningcited by 12
  7. 2022A Survey of Multi-Agent Human-Robot Interaction Systemscited by 10
  8. 2019Scalable Decision-Theoretic Planning in Open and Typed Multiagent Systemscited by 7
  9. 2013Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agentscited by 5
  10. 2021Marginal MAP Estimation for Inverse RL under Occlusion with Observer Noisecited by 3
  11. 2022Semi-Supervised Imitation Learning of Team Policies from Suboptimal Demonstrationscited by 2
  12. 2024DECAF: a Discrete-Event based Collaborative Human-Robot Framework for Furniture Assemblycited by 2

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  1. 2024ARMCHAIR: integrated inverse reinforcement learning and model predictive control for human-robot collaboration
  2. 2024Enabling Multi-Robot Collaboration from Single-Human Guidance
  3. 2018An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning
  4. 2026HALO: Learning Human-Robot Collaboration via Heterogeneous-Agent Lyapunov Policy Optimization
  5. 2026Adaptive Collaboration with Humans: Metacognitive Policy Optimization for Multi-Agent LLMs with Continual Learning
  6. 2026A Scoping Review and Experimental Study on Reinforcement Learning from Human Feedback for Human-Robot Collaboration
  7. 2024Large Language Model-based Human-Agent Collaboration for Complex Task Solving
  8. 2023Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning