IOC Based Trajectory Generation to Increase Human Acceptance of Robot Motions in Collaborative Tasks

Abstract Collaboration between humans and robots is an important aspect of Industry 4.0. It can be improved by incorporating human-like characteristics into robot motion planning. It is assumed that humans move optimal with respect to a certain objective or cost function. To find this function, also for a robot, we use an inverse optimal control approach identifying what linear weighted combination of physically interpretable cost functions best mimics human point-to-point motions. A bi-level optimization is used, where the upper level compares the optimal robot result of the lower level with human reference motions. Two depth cameras are combined in a setup to record these reference motions. The resulting weighted cost functions are then used to generate new motions for a seven degrees of freedom robot arm. The resulting optimized motions are compared to standard robot motions based on linear interpolation in joint or task space. The comparison is performed by means of a small experiment where preliminary observations show that humans experience these motions as more anthropomorphic and feel at least equally comfortable and safe compared to existing motion planning strategies.