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Dynamic movement primitives are widely used for learning skills which can bedemonstrated to a robot by a skilled human or controller. While theirgeneralization capabilities and simple formulation make them very appealing touse, they possess no strong guarantees to satisfy operational safetyconstraints for a task. In this paper, we present constrained dynamic movementprimitives (CDMP) which can allow for constraint satisfaction in the robotworkspace. We present a formulation of a non-linear optimization to perturb theDMP forcing weights regressed by locally-weighted regression to admit a ZeroingBarrier Function (ZBF), which certifies workspace constraint satisfaction. Wedemonstrate the proposed CDMP under different constraints on the end-effectormovement such as obstacle avoidance and workspace constraints on a physicalrobot. A video showing the implementation of the proposed algorithm usingdifferent manipulators in different environments could be found herehttps://youtu.be/hJegJJkJfys.

Constrained Dynamic Movement Primitives for Safe Learning of Motor Skills