Human-robot cooperation through force adaptation using dynamic motion primitives and iterative learning

Abstract

Human robot cooperation is a challenging task of future generation home robots and is relevant also for industrial applications, where robots are supposed to act together with humans in non-structured environments. The paper focuses on on-line adaptation of robot trajectories, where robots and humans are autonomous agents coupled only through the manipulated object. Within the proposed approach, the robot adapts to the human intentions through the sensory feedback, where safety is one of the most important issues. The algorithm is based on representation of trajectories with the Dynamic Movement Primitives, where the adaptation of the corresponding robot trajectories relies on the Iterative Learning Controller framework. In order to demonstrate the effectiveness of the proposed approach, we applied two KUKA LWR robots in a bimanual human-robot collaborative task.