Robot Weightlifting By Direct Policy Search

This paper describes a method for structuring a robot motor learning task. By designing a suitably parameterized policy, we show that a simple search algorithm, along with biologically motivated constraints, offers an effective means for motor skill acquisition. The framework makes use of the robot counterparts to several elements found in human motor learning: imitation, equilibrium-point control, motor programs, and synergies. We demonstrate that through learning, coordinated behavior emerges from initial, crude knowledge about a difficult robot weightlifting task.