  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.
