The main result of this paper is an approach to learning parametrized sets of dynamic movement primitives based on a library of example movements. Learning was implemented by applying locally weighted regression where the goal of an action is used as a query point into the library of example movements. The proposed approach enables the generation of a wide range of movements that are adapted to the current configuration of the external world without requiring an expert to appropriately modify the underlying differential equations to account for perceptual feedback.
COBISS.SI-ID: 23181863
In this paper we explore how to apply reinforcement learning to modify the subgoals of primitive movements involved in the given task. As the underlying sensorimotor representation we selected nonlinear dynamic systems, which provide a powerful machinery for the modification of motion trajectories. We propose a new formulation for dynamic systems, which ensures that consecutive primitive movements can be splined together in a continuous way (up to second order derivatives).
COBISS.SI-ID: 23174695