ArrayBot：通過觸覺實現通用分佈式操作的強化學習

摘要

我們介紹了 ArrayBot，這是一個分佈式操作系統，由一個 16x16 的垂直滑動支柱陣列和觸覺感測器組成，可以同時支撐、感知和操作桌面上的物體。為了實現通用的分佈式操作，我們利用強化學習 (RL) 演算法來自動發現控制策略。面對極度冗餘的操作，我們提出通過考慮空間局部操作區域和頻域中的低頻操作，來重塑動作空間。憑藉這種重塑的動作空間，我們訓練了可以僅通過觸覺觀察重新定位各種物體的 RL 智能體。令人驚訝的是，我們發現發現的策略不僅可以推廣到模擬器中看不見的物體形狀，還可以在不進行任何領域隨機化的情況下轉移到實體機器人上。利用部署的策略，我們展示了豐富的現實世界操作任務，展示了強化學習在 ArrayBot 上進行分佈式操作的巨大潛力。

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：通過觸覺實現通用分佈式操作的強化學習

ArrayBot: Reinforcement Learning for Generalizable Distributed Manipulation through Touch

摘要

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