Optimal robot motions for physical criteria

Abstract This paper presents an optimization‐based framework for emulating the low‐level capabilities of human motor coordination and learning. Our approach rests on the observation that in most biological motor learning scenarios some form of optimization with respect to a physical criterion is taking place. By appealing to techniques from the theory of Lie groups, we are able to formulate the equations of motion of complex multibody systems in such a way that the resulting optimization problems can be solved reliably and efficiently—the key lies in the ability to compute exact analytic gradients of the objective function without resorting to numerical approximations. The methodology is illustrated via a wide range of optimized, “natural” motions for robots performing various human‐like tasks—for example, power lifting, diving, and gymnastics. © 2001 John Wiley & Sons, Inc.