An Optimization-Based Estimation and Adaptive Control Approach for Human-Robot Cooperation

This paper presents a novel robot programming approach for actively assisting humans in human-robot cooperation tasks. First, the paper discusses an invariant description-based parametric modeling approach for six degree-of-freedom motion trajectories. This generic approach facilitates building a library of motion models in a systematic way. Second, the paper presents a constrained optimization-based parameter estimation technique for estimating the motion model parameters. Both batch and recursive schemes are presented. Third, the paper presents a control architecture based on our constraint-based task specification approach iTaSC that supports including secondary task objectives or inequality constraints (for example joint limits) in the robot task definition. The control architecture is exemplified using the KUKA LWR 4 robot and Orocos robot control software. Experimental results clearly indicate the potential of the approach by showing significant lower human-robot interaction forces compared to classical admittance control.