ArrayBot: Reinforcement Learning for Generalizable Distributed
  Manipulation through Touch
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.
649e68001b00eaf1eeb1db68
Zhengrong Xue
649e68001b00eaf1eeb1db69
Han Zhang
649e68001b00eaf1eeb1db6a
Jingwen Cheng
649e68001b00eaf1eeb1db6b
Zhengmao He
649e68001b00eaf1eeb1db6c
Yuanchen Ju
649e68001b00eaf1eeb1db6d
Changyi Lin
claimed_verified
2024-09-19T07:08:57.315Z
663d635261d90caa6c4d0abf
/avatars/aebc16bd138f92160ae729d4cf42121b.svg
Changyi Lin
user
linchangyi1
649e68001b00eaf1eeb1db6e
Gu Zhang
claimed_verified
2024-03-07T09:18:03.755Z
65e95effe19214e9d13d7a2a
/avatars/35ae40579ac53d7b598957be9c8b4710.svg
Gu Zhang
user
GuZhang
649e68001b00eaf1eeb1db6f
Huazhe Xu
admin_assigned
2023-06-30T19:41:14.892Z
649c130f92525b3ac9fbcffe
/avatars/b60e4d6803f7e4ef42200ddcb9e5bac1.svg
Huazhe Xu
user
Huazhe-Xu
ArrayBot: Reinforcement Learning for Generalizable Distributed
  Manipulation through Touch
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 uses reinforcement learning to discover control policies for distributed manipulation of tabletop objects through tactile feedback, achieving generalization to unseen objects in both simulated and real-world settings.
reinforcement learning RL action space spatially local action patch low-frequency actions tactile observations policy discovery domain randomization real-world manipulation tasks distributed manipulation