Constrained Dynamic Movement Primitives for Safe Learning of Motor Skills
This work addresses safety in robot learning for tasks like manipulation, but it is incremental as it builds on existing DMP methods by adding constraint guarantees.
The paper tackles the problem of ensuring operational safety constraints in dynamic movement primitives (DMPs) for robot motor skills by introducing constrained dynamic movement primitives (CDMPs), which use a non-linear optimization and Zeroing Barrier Functions to guarantee workspace constraint satisfaction, demonstrated on a physical robot with obstacle avoidance and workspace constraints.
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.