On-Robot Learning With Equivariant Models

Dian Wang, Mingxi Jia, Xupeng Zhu, Robin Walters, Robert Platt

Keywords: Manipulation, Reinforcement Learning, Equivariance

TL;DR: This paper demonstrates sample-efficient on-robot learning in manipulation using equivariant models.

Abstract: Recently, equivariant neural network models have been shown to improve sample efficiency for tasks in computer vision and reinforcement learning. This paper explores this idea in the context of on-robot policy learning in which a policy must be learned entirely on a physical robotic system without reference to a model, a simulator, or an offline dataset. We focus on applications of Equivariant SAC to robotic manipulation and explore a number of variations of the algorithm. Ultimately, we demonstrate the ability to learn several non-trivial manipulation tasks completely through on-robot experiences in less than an hour or two of wall clock time.

Student First Author: yes

Website: https://pointw.github.io/equi_robot_page/

Code: https://github.com/pointW/equi_rl