Modeling of human robot interaction and use of humanoid robots for rehabilitation training
Human motor control is compliant, i.e. the muskuloscelatal system is elstic, and this elasticity is exploited during control. Compliant control is a complex problem for present humanoid robots, and it is critical for a lot of actions with high relevant for everyday life, such as soft catching, soft reception when falling, or sliding and pushing large objects as well as joint actions performed in teams such as manipulation of large scale objects by multiple humans. The overarching objective of the CogIMon project is to advance key technologies that lead to a step-change in cognitive compliant interaction in human-robot teams, integrating physical human-robot interaction, visually guided manipulation and safety integrated design in a systematic way.
Our group will focus in this project on decipering human interpersonal sensorimotor strategies, and how they accomplish predictive control in interactive tasks. This requires a better understanding of the cognitive aspects that are essential for the control of compliant motion, i.e. extraction and prediction of task-relevant information from the motion (kinematics) of the interaction partner and the generation of predictive models of partner’s motions using kinematic motion tracking data. Our group will also work in a leadign role on one application scenario, where a compliant humanoid robot will be used for the training of patients in interactive tasks, such as throwing and catching in interactive games.