ArrayBot：通过触觉实现通用分布式操作的强化学习

摘要

我们介绍了ArrayBot，这是一个分布式操作系统，由一个16×16的垂直滑动柱阵列与触觉传感器集成而成，可以同时支持、感知和操作桌面上的物体。为了实现通用的分布式操作，我们利用强化学习（RL）算法自动发现控制策略。面对大量冗余动作，我们提出通过考虑空间局部动作块和频域中的低频动作来重塑动作空间。通过这种重塑的动作空间，我们训练RL代理程序，可以仅通过触觉观察重新定位各种物体。令人惊讶的是，我们发现发现的策略不仅可以推广到模拟器中看不见的物体形状，而且可以在不进行任何领域随机化的情况下转移到物理机器人上。利用部署的策略，我们展示了丰富的现实世界操作任务，展示了RL在ArrayBot上进行分布式操作的巨大潜力。

English

We present ArrayBot, a distributed manipulation system consisting of a 16 times 16 array of vertically sliding pillars integrated with tactile sensors, which can simultaneously support, perceive, and manipulate the tabletop objects. Towards generalizable distributed manipulation, we leverage reinforcement learning (RL) algorithms for the automatic discovery of control policies. In the face of the massively redundant actions, we propose to reshape the action space by considering the spatially local action patch and the low-frequency actions in the frequency domain. With this reshaped action space, we train RL agents that can relocate diverse objects through tactile observations only. Surprisingly, we find that the discovered policy can not only generalize to unseen object shapes in the simulator but also transfer to the physical robot without any domain randomization. Leveraging the deployed policy, we present abundant real-world manipulation tasks, illustrating the vast potential of RL on ArrayBot for distributed manipulation.

ArrayBot：通过触觉实现通用分布式操作的强化学习

ArrayBot: Reinforcement Learning for Generalizable Distributed Manipulation through Touch

摘要

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