title: ArrayBot: Reinforcement Learning for Generalizable Distributed
    Manipulation through Touch
summary: 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.
paper_authors._id: 649e68001b00eaf1eeb1db68
paper_authors.name: Zhengrong Xue
paper_authors._id: 649e68001b00eaf1eeb1db69
paper_authors.name: Han Zhang
paper_authors._id: 649e68001b00eaf1eeb1db6a
paper_authors.name: Jingwen Cheng
paper_authors._id: 649e68001b00eaf1eeb1db6b
paper_authors.name: Zhengmao He
paper_authors._id: 649e68001b00eaf1eeb1db6c
paper_authors.name: Yuanchen Ju
paper_authors._id: 649e68001b00eaf1eeb1db6d
paper_authors.name: Changyi Lin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-19T07:08:57.315Z
paper_authors._id: 663d635261d90caa6c4d0abf
paper_authors.avatarUrl: /avatars/aebc16bd138f92160ae729d4cf42121b.svg
paper_authors.fullname: Changyi Lin
paper_authors.type: user
paper_authors.user: linchangyi1
paper_authors._id: 649e68001b00eaf1eeb1db6e
paper_authors.name: Gu Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-07T09:18:03.755Z
paper_authors._id: 65e95effe19214e9d13d7a2a
paper_authors.avatarUrl: /avatars/35ae40579ac53d7b598957be9c8b4710.svg
paper_authors.fullname: Gu Zhang
paper_authors.type: user
paper_authors.user: GuZhang
paper_authors._id: 649e68001b00eaf1eeb1db6f
paper_authors.name: Huazhe Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-30T19:41:14.892Z
paper_authors._id: 649c130f92525b3ac9fbcffe
paper_authors.avatarUrl: /avatars/b60e4d6803f7e4ef42200ddcb9e5bac1.svg
paper_authors.fullname: Huazhe Xu
paper_authors.type: user
paper_authors.user: Huazhe-Xu
paper_title: ArrayBot: Reinforcement Learning for Generalizable Distributed
    Manipulation through Touch
paper_summary: 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.
paper_ai_keywords: reinforcement learning (RL) algorithms control policies action space spatially local action patch frequency domain tactile observations domain randomization distributed manipulation