发表机构
ETH Zürich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出将随机技能集成到基于采样的多机器人规划中，通过马尔可夫决策过程实现动态协调，避免保守基线，提升鲁棒性。
AI 中文摘要
随着机器人越来越多地以群体形式部署并共享工作空间以执行现实世界任务，规划它们围绕复杂操作技能的并发运动变得至关重要。这些技能涉及连续的物理执行，并可能表现出随机行为，导致执行时间可变和连续轨迹不确定。现有规划器要么将执行限制在单机器人场景，要么依赖开环路径，要么使用事后调度，从而阻碍动态协调。在本文中，我们通过将问题表述为多模态复合道路图上的马尔可夫决策过程（MDP），将随机技能集成到基于采样的多机器人规划中，以解决这一差距。对于随机技能，求解MDP产生一个反应式策略，允许可控机器人动态调整其运动以响应其他机器人对操作技能的执行。通过在规划时直接解决技能不确定性，该方法避免了保守基线的悲观性，并实现了鲁棒、动态的多机器人协调。规划器的代码可在该https URL获得。
英文摘要
As robots are increasingly deployed in groups and share workspaces to execute real-world tasks, planning their concurrent motions around complex manipulation skills becomes essential. These skills involve continuous physical execution and may exhibit stochastic behavior, resulting in variable execution times and uncertain continuous trajectories. Existing planners either limit execution to single-robot scenarios, rely on open-loop paths, or use post-hoc scheduling that prevents dynamic coordination. In this paper, we address this gap by integrating stochastic skills into sampling-based multi-robot planning by formulating the problem as a Markov Decision Process (MDP) over a multi-modal composite roadmap. For stochastic skills, solving the MDP yields a reactive policy that allows controllable robots to dynamically adapt their motions in response to other robots' execution of manipulation skills. By resolving skill uncertainty directly at planning time, this approach avoids the pessimism of conservative baselines and unlocks robust, dynamic multi-robot coordination. Code for the planners is available at https://www.vhartmann.com/stochastic-skills.
Comments8 pages, 5 figures, 2 tables
