Approaches for Learning Human-like Motor Skills which Require Variable Stiffness During Execution

Humans employ varying stiffness in everyday life for almost all human motor skills, using both passive and active compliance. Robots have only recently acquired variable passive stiffness actuators and they are not yet mature. Active compliance controllers have existed for a longer time, but the problem of automatic determination of the necessary compliance to achieve a task has not been thoroughly studied. Teaching humanoid robots to apply variable stiffness to the skills they acquire is vital in order to achieve human-like naturalness of the execution. Also, using adaptive compliance can help to increase the energy efficiency. This paper compares two different approaches that allow robots to learn human-like skills which require varying stiffness during execution. The advantages and disadvantages of each approach is discussed and demonstrated with various experiments on an activelycompliant Barrett WAM robot. keywords: variable stiffness, motor skills, imitation learning, reinforcement learning, programming by demonstration, kinesthetic teaching, pHRI