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.

翻译：我们提出ArrayBot，一种由集成触觉传感器的$16 \times 16$垂直滑动柱阵列组成的分布式操作系统，能够同时支撑、感知并操作桌面物体。为实现可泛化的分布式操作，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补丁和频域中的低频动作来重塑动作空间。借助这一重塑后的动作空间，我们训练了仅通过触觉观测即可重新定位多样化物体的RL智能体。令人惊讶的是，我们发现所发现策略不仅能泛化到模拟器中未见过的物体形状，还能在无需任何域随机化的情况下迁移至物理机器人。利用部署的策略，我们展示了丰富的真实世界操作任务，揭示了RL在ArrayBot上实现分布式操作的巨大潜力。