Learning at the level of synergies for a robot weightlifter

Human motor coordination is associated with “synergies” in the neuromuscular system that cause our bodies to behave as coordinated units rather than collections of decoupled degrees of freedom. The focus of this paper is the role that synergies play for robot motor coordination, and in particular, we use a weightlifting task to demonstrate how trial-and-error learning can cause synergies to evolve from a simple control system and crude kinematic path. We also show how a robot learns to exploit its intrinsic dynamics, thereby improving performance, as individual joint motions become actively coupled via the control system.