ArrayBot: Reinforcement Learning for Generalizable Distributed Manipulation through Touch

06/29/2023

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We present ArrayBot, a distributed manipulation system consisting of a 16 × 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.

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ArrayBot: Reinforcement Learning for Generalizable Distributed Manipulation through Touch

Sim-to-Real Model-Based and Model-Free Deep Reinforcement Learning for Tactile Pushing

Learning to Manipulate Deformable Objects without Demonstrations

Bi-Touch: Bimanual Tactile Manipulation with Sim-to-Real Deep Reinforcement Learning

Exploiting Intrinsic Stochasticity of Real-Time Simulation to Facilitate Robust Reinforcement Learning for Robot Manipulation

Linear Delta Arrays for Compliant Dexterous Distributed Manipulation

ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation

Visuo-Tactile Manipulation Planning Using Reinforcement Learning with Affordance Representation

ArrayBot: Reinforcement Learning for Generalizable Distributed Manipulation through Touch

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