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：人类在接触密集任务（如装载洗碗机或整理书架）中自然地利用触觉反馈。当前的机器人系统专注于避免意外接触，通常会依赖策略性布置的环境传感器。近年来，接触利用型操作策略已在仿真中训练并部署到真实机器人上。然而，它们需要某种形式的真实环境适应性调整来弥合仿真到现实的差距，这并非在所有场景中都可行。本文针对将盘子装入槽架这一接触密集的家务任务，在仿真中训练了一种接触利用型操作策略，该策略无需任何微调即可迁移到真实机器人。我们研究了实现这种零样本迁移所需的各种因素，如时延建模、记忆表示和环境随机化。我们的策略以最小的仿真-真实差距实现迁移，并显著优于启发式和学习型基线方法。该策略还能泛化到不同尺寸和重量的盘子。演示视频和代码可在 https://sites.google.com/view/compliant-object-insertion 获取。