Automatic Parameterization of Motion and Force Controlled Robot Skills

Abstract

Compliant robot tasks such as grinding require a robot to use a specific control strategy and to consider a number of process parameters. It is demanding to program such behaviors from scratch. Therefore, so called contact skills can be employed that are pre-programmed control strategies, which are optimized for the intended task. With that level of abstraction, which is defining skills that are specific to the task, only the skill's parameters need to be identified and not the whole strategy to be implemented. In order to allow non-experts to transfer such complex behaviors to a robot, we present two different contact skills and how they are automatically parameterized by a human demonstration. This process learns the robot behavior in one shot while considering task goals, such as desired forces and motions. We evaluated our framework in the PyBullet physics simulator and showed that the parameterized skills follow the task goals while generalizing to changes in the environment.