Neural Learning of Vector Fields for Encoding Stable Dynamical Systems

Lemme A, Reinhart F, Neumann K, Steil JJ. Neural Learning of Vector Fields for Encoding Stable Dynamical Systems. Neurocomputing. 2014;141:3-14

Reactive phase and task space adaptation for robust motion execution

Abstract — An essential aspect for making robots succeed in real-world environments is to give them the ability to ro-bustly perform motions in continuously changing situations. Classical motion planning methods usually create plans for static environments. The direct execution of such plans in dynamic environments often becomes problematic. We present an approach that adapts motion plans by feeding changes of the environment into a transformation of the plan in task space. Furthermore, the progress in the plan is defined with a phase variable that is updated adaptively according to the actual task progress. This phase variable releases the strict time compliance that many motion planning methods bring along. The main benefit of our approach is the ability to do this adaptation in a computational efficient manner during the execution of the motion. Thus, the gap between the motion planning and motion execution stage is bridged by continuously transforming geometric and dynamic features of a reference plan to the current situation. We evaluate the performance of our approach by comparing it to alternative methods such as dynamic motion primitives and continuous replanning on several simulated benchmark tasks. Moreover, we demonstrate the real robot applicability on a PR2 robot platform. I

Bootstrapping movement primitives from complex trajectories

Lemme A. Bootstrapping movement primitives from complex trajectories. Bielefeld: Bielefeld University; 2014