A Dynamical Systems Approach to Adaptive Sequencing of Movement Primitives

Abstract

This paper introduces a control concept for motion generation of redundant robots based on combinations of movement primitives (MP). It addresses the question of how to create continuous and smooth sequences of actions or transitions between different motion skills while avoiding the necessity of recurrent planning. MPs are defined on task coordinates and modeled as dynamical systems with attractor behavior featuring additional signals to ease their coordination. Sequences and transitions between skills are realized in a unified way as bifurcating dynamical systems based on continuous-time recurrent neural networks. The neural output is used as activation signal for MPs. It is shown how the parameters of these dynamical systems can be chosen to generate a desired behavior. First results are shown in a physical simulation environment on a high-DoF robot with human-like upper body. The system can create smooth transients of MPs in sequences as well as during changes of strategies, notably showing more than only local adaptation capabilities. Keywords: adaptive control, movement primitives, dynamical systems