Constrained Dynamic Movement Primitives for Safe Learning of Motor Skills

TL;DR

This paper introduces constrained dynamic movement primitives (CDMP) that incorporate safety constraints into robot skill learning by using a non-linear optimization with Zeroing Barrier Functions, ensuring safe workspace operation.

Contribution

The paper proposes a novel formulation of DMPs with integrated workspace safety constraints using ZBFs, enabling safe robot skill learning and execution.

Findings

Successfully enforced obstacle avoidance in robot movements.

Validated the approach on different manipulators and environments.

Demonstrated real-world applicability with physical robots.

Abstract

Dynamic movement primitives are widely used for learning skills which can be demonstrated to a robot by a skilled human or controller. While their generalization capabilities and simple formulation make them very appealing to use, they possess no strong guarantees to satisfy operational safety constraints for a task. In this paper, we present constrained dynamic movement primitives (CDMP) which can allow for constraint satisfaction in the robot workspace. We present a formulation of a non-linear optimization to perturb the DMP forcing weights regressed by locally-weighted regression to admit a Zeroing Barrier Function (ZBF), which certifies workspace constraint satisfaction. We demonstrate the proposed CDMP under different constraints on the end-effector movement such as obstacle avoidance and workspace constraints on a physical robot. A video showing the implementation of the proposed algorithm using different manipulators in different environments could be found here https://youtu.be/hJegJJkJfys.