Cognitive Feedforward Learning Control for Object Manipulation with a Power Assist Robotic System

Abstract

In the first step, a 1-DOF power assist robotic system (PARS) was developed for object manipulation with it, and the dynamics for human-robot co-manipulation of objects was derived reflecting human cognition (weight perception). Then, an admittance control scheme with position feedback and velocity controller was derived from the weight-perception-based dynamics. In a user study, human subjects lifted objects with the system. An evaluation scheme was developed to evaluate human-robot interaction (HRI) and co-manipulation performance. A reinforcement learning method was implemented to learn the admittance control parameters resulting in satisfactory HRI and manipulation performance. The results showed that inclusion of weight perception in the dynamics and the learning control were effective to produce satisfactory HRI and performance. In the second step, a novel variable admittance feedforward adaptive control algorithm was proposed, which helped further improve the HRI and manipulation performance. Then, effectiveness of the adaptive feedforward learning control method was validated using a multi-DOF PARS for manipulating heavy objects.