提出开放人机协作新框架，让机器人灵活进出任务
Open Human-Robot Collaboration using Decentralized Inverse Reinforcement Learning
- 设计oDec-MDP框架，支持人机动态加入退出协作
- 基于Dec-AIRL方法在消防与装配任务中提升协作效率
- 适合需灵活调度人机资源的实际场景应用
近年来，人机协作（HRC）领域发展迅速，但多数研究将协作视为封闭系统，要求所有参与者全程参与。然而，许多实际场景中，人类无需全程在场。本文提出新型多智能体框架oDec-MDP，专门建模可动态进出的任务场景。将最近的Dec-AIRL方法推广至开放系统，实现对开放协作行为的学习。实验在简化消防任务和真实的人-机协同装配任务中验证，结果表明该框架相比封闭系统显著提升协作性能。
原文摘要 · Abstract (English)
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.
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