Feedback motion planning and learning from demonstration in physical robotic assistance: differences and synergies

Abstract

Goal-directed physical assistance to the human is one of the most challenging problems in the area of human-robot interaction. Planning and learning from demonstration represent two conceptually different approaches to achieve goal-directed behavior. Here we examine the properties of a planning-based and a learning-based approach in the context of physical robotic assistance for the prototypical task of cooperative object maneuvering. In order to exploit the complementary strengths of planning and learning-based approaches we derive three novel synergy strategies. The algorithms are experimentally evaluated in a human user study in a planar virtual-reality scenario and in a proof-of-concept study with a human-sized mobile robot with two 7DoF arms. The results show that combinations of planning and learning algorithms are superior over the individual approaches.