Efficient learning of constraints and generic null space policies

Abstract

A large class of motions can be decomposed into a movement task and null-space policy subject to a set of constraints. When learning such motions from demonstrations, we aim to achieve generalisation across different unseen constraints and to increase the robustness to noise while keeping the computational cost low. There exists a variety of methods for learning the movement policy and the constraints. The effectiveness of these techniques has been demonstrated in low-dimensional scenarios and simple motions. In this paper, we present a fast and accurate approach to learning constraints from observations. This novel formulation of the problem allows the constraint learning method to be coupled with the policy learning method to improve policy learning accuracy, which enables us to learn more complex motions. We demonstrate our approach by learning a complex surface wiping policy in a 7-DOF robotic arm.