Learning Parametric Dynamic Movement Primitives from Multiple Demonstrations

This paper proposes a novel approach to learn highly scalable Control Policies (CPs) of basis movement skills from multiple demonstrations. In contrast to conventional studies with a. single demonstration, i.e., Dynamic Movement Primitives (DMPs) [1], our approach efficiently encodes multiple demonstrations by shaping a. parametric-attractor landscape in a set of differential equations. This approach allows the learned CPs to synthesize novel movements with novel motion styles by specifying the linear coefficients of the bases as parameter vectors without losing useful properties of DMPs, such as stability and robustness against perturbations. For both discrete and rhythmic movement skills, we present a unified learning procedure for learning a parametric-attractor landscape from multiple demonstrations. The feasibility and highly extended