用力场与示范数据生成仿真轨迹，提升机器人接触任务的柔顺性。
Flow with the Force Field: Learning 3D Compliant Flow Matching Policies from Force and Demonstration-Guided Simulation Data
- 基于人类示范和力场信息生成仿真实验数据
- 在真实机器人上实现可靠接触维持与新环境适应
- 适合需要精确力控的复杂抓取与操作任务
尽管视觉-运动策略近年取得进展，但高接触密度任务仍具挑战。需持续接触的任务要求显式处理柔顺性和力控，然而多数视觉-运动策略忽略柔顺性，常导致接触力过大或在不确定性下行为脆弱。将力信息引入视觉模仿学习可增强对接触的认知，但通常需大量数据。为缓解数据稀缺问题，可通过仿真生成数据，但高质量仿真需高昂计算成本，易产生Sim2Real差距。本文提出一种框架，仅需一次人类示范即可生成含力信息的仿真数据，并证明结合柔顺策略能显著提升从合成数据中学习的视觉-运动策略性能。我们在真实机器人上验证了该方法，涵盖非抓握式方块翻转与双臂物体移动任务，所学策略表现出可靠的接触保持能力并能适应新条件。
原文摘要 · Abstract (English)
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/
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