Sample Efficient Grasp Learning Using Equivariant Models

In planar grasp detection, the goal is to learn a function from an image of a scene onto a set of feasible grasp poses in SE(2). In this paper, we recognize that the optimal grasp function is SE(2)-equivariant and can be modeled using an equivariant convolutional neural network. As a result, we are able to significantly improve the sample efficiency of grasp learning, obtaining a good approximation of the grasp function after only 600 grasp attempts. This is few enough that we can learn to grasp completely on a physical robot in about 1.5 hours.

In Robotics:Science and Systems (RSS 2022), New York, USA. (Also presented in RLDM 2022 & ICRA 2022 Workshop on Scaling Robot Learning, Spotlight)