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/

翻译：尽管视觉运动策略近年来取得了进展，但接触密集型任务仍然是一个挑战。需要持续接触的机器人操作任务要求对顺应性和力进行显式处理。然而，大多数视觉运动策略忽视顺应性，忽略了与真实世界物理交互的重要性，常常导致接触力过大或在不确定性下表现出脆弱行为。将力信息引入基于视觉的模仿学习有助于提升接触感知能力，但也可能需要大量数据才能取得良好效果。缓解数据稀缺的一种方法是在仿真中生成数据，但需要计算密集型处理才能生成足够优质的数据以避免仿真到现实的差距。在本工作中，我们提出了一种在仿真中生成力信息数据的框架，该框架通过单次人类演示实例化，并展示了与顺应性策略耦合如何提升从合成数据学习的视觉运动策略的性能。我们在真实机器人任务上验证了所提方法，包括非抓取式方块翻转和双手物体移动任务，学习到的策略展现出可靠的接触保持能力以及对新条件的适应性。项目网站：https://flow-with-the-force-field.github.io/webpage/