Compact models of human reaching motions for robotic control in everyday manipulation tasks

Autonomous personal robots are currently being equipped with hands and arms that have kinematic redundancy similar to those of humans. Humans exploit the redundancy in their motor system by optimizing secondary criteria. Tasks which are executed repeatedly lead to movements that are highly optimized over time, which leads to stereotypical and pre-planned motion patterns. This stereotypical motion can be modeled well with compact models, as has been shown for locomotion. In this paper, we determine compact models for human reaching and obstacle avoidance in everyday manipulation tasks, and port these models to an articulated robot. We acquire compact models by analyzing human reaching data acquired with a magnetic motion tracker with dimensionality reduction and clustering methods. The stereotypical reaching trajectories so acquired are used to train a Dynamic Movement Primitive, which is executed on the robot. This enables the robot not only to follow these trajectories accurately, but also uses the compact model to predict and execute further human trajectories.