流体与力场：从力和示范引导的仿真数据中学习3D顺应性流匹配策略
Flow with the Force Field: Learning 3D Compliant Flow Matching Policies from Force and Demonstration-Guided Simulation Data
AI总结：
本文提出一种框架，通过单人示范生成力引导的仿真数据，结合顺应性策略提升视觉模仿学习的性能，验证了在真实机器人任务中的可靠性与适应性。
摘要：
尽管视觉-运动策略近年来取得了进展，但接触密集的任务仍是一个挑战。需要连续接触的机器人操作任务需要显式处理顺应性和力。然而，大多数视觉-运动策略忽略顺应性，忽视了与现实世界物理交互的重要性，常导致过度接触力或在不确定性下行为脆弱。将力信息引入基于视觉的模仿学习可提高对接触的意识，但可能需要大量数据才能表现良好。解决数据稀缺的一种方法是生成仿真数据，但需要计算上昂贵的过程来生成足够好的数据以避免Sim2Real差距。在本文中，我们提出了一种生成力引导数据的框架，通过单人示范实例化，并展示如何将该框架与顺应性策略结合，提高从合成数据中学习的视觉-运动策略的性能。我们验证了我们的方法在真实机器人任务中的有效性，包括非抓取块翻转和双手物体移动，其中学习的策略表现出可靠的接触维持和对新条件的适应性。项目网站：https://flow-with-the-force-field.github.io/webpage/
英文摘要：
While visuomotor policy has made advancements in recent years, contact-rich tasks still remain a challenge. Robotic manipulation tasks that require continuous contact demand explicit handling of compliance and force. However, most visuomotor policies ignore compliance, overlooking the importance of physical interaction with the real world, often leading to excessive contact forces or fragile behavior under uncertainty. Introducing force information into vision-based imitation learning could help improve awareness of contacts, but could also require a lot of data to perform well. One remedy for data scarcity is to generate data in simulation, yet computationally taxing processes are required to generate data good enough not to suffer from the Sim2Real gap. In this work, we introduce a framework for generating force-informed data in simulation, instantiated by a single human demonstration, and show how coupling with a compliant policy improves the performance of a visuomotor policy learned from synthetic data. We validate our approach on real-robot tasks, including non-prehensile block flipping and a bi-manual object moving, where the learned policy exhibits reliable contact maintenance and adaptation to novel conditions. Project Website: https://flow-with-the-force-field.github.io/webpage/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
