Humans naturally exploit haptic feedback during contact-rich tasks like loading a dishwasher or stocking a bookshelf. Current robotic systems focus on avoiding unexpected contact, often relying on strategically placed environment sensors. Recently, contact-exploiting manipulation policies have been trained in simulation and deployed on real robots. However, they require some form of real-world adaptation to bridge the sim-to-real gap, which might not be feasible in all scenarios. In this paper we train a contact-exploiting manipulation policy in simulation for the contact-rich household task of loading plates into a slotted holder, which transfers without any fine-tuning to the real robot. We investigate various factors necessary for this zero-shot transfer, like time delay modeling, memory representation, and domain randomization. Our policy transfers with minimal sim-to-real gap and significantly outperforms heuristic and learnt baselines. It also generalizes to plates of different sizes and weights. Demonstration videos and code are available at https://sites.google.com/view/compliant-object-insertion.

翻译：人类在完成接触密集型任务（例如装载洗碗机或整理书架）时，会自然地利用触觉反馈。当前的机器人系统往往致力于避免意外接触，常依赖策略性放置的环境传感器。近年来，接触利用型操控策略已在仿真环境中训练并部署到真实机器人上。然而，这些策略需要某种形式的真实世界适配以弥合仿真到现实的差距，这在某些场景下可能难以实现。本文针对将盘子放入带槽支架这一接触密集型的家务任务，在仿真环境中训练了一种接触利用型操控策略，该策略无需任何微调即可迁移至真实机器人。我们研究了实现此零样本迁移所需的各种因素，例如时延建模、记忆表征和域随机化。该策略的迁移过程实现了极小的仿真-现实差距，并显著优于基于启发式方法和学习的基线。此外，它还能泛化到不同尺寸和重量的盘子。演示视频及代码见 https://sites.google.com/view/compliant-object-insertion。