Learning Force-Relevant Skills from Human Demonstration

Abstract EN

Many human manipulation skills are force relevant, such as opening a bottle cap and assembling furniture.

However, it is still a difficult task to endow a robot with these skills, which largely is due to the complexity of the representation and planning of these skills.

This paper presents a learning-based approach of transferring force-relevant skills from human demonstration to a robot.

First, the force-relevant skill is encapsulated as a statistical model where the key parameters are learned from the demonstrated data (motion, force).

Second, based on the learned skill model, a task planner is devised which specifies the motion and/or the force profile for a given manipulation task.

Finally, the learned skill model is further integrated with an adaptive controller that offers task-consistent force adaptation during online executions.

The effectiveness of the proposed approach is validated with two experiments, i.e., an object polishing task and a peg-in-hole assembly.