Constrained Dynamic Movement Primitives for Safe Learning of Motor Skills

• URL: http://arxiv.org/abs/2209.14461v1
• Date: Wed, 28 Sep 2022 22:59:33 GMT
• Title: Constrained Dynamic Movement Primitives for Safe Learning of Motor Skills
• Authors: Seiji Shaw, Devesh K. Jha, Arvind Raghunathan, Radu Corcodel, Diego Romeres, George Konidaris and Daniel Nikovski
• Abstract summary: We present constrained dynamic movement primitives (CDMP) which can allow for constraint satisfaction in the robot workspace. A video showing the implementation of the proposed algorithm using different manipulators in different environments could be found here.
• Score: 25.06692536893836
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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