Dynamic Programming
Properties of Synergies Arising from a Theory of Optimal Motor Behavior
Neural Computation
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Controlling human-like robots with musculoskeletal structure has been a challenging problem in engineering. In biological studies, motor synergy hypothesis has been proposed as a solution in order to control high degree-of-freedom and complex human body. In this paper, we focus on exploring the applicability of motor synergies in generating goal-directed movements by optimal control in a human-like robotic arm. We focus on three problems: 1) Can motor synergies facilitate the solving of optimal control problem? 2) What properties should motor synergies have in order to achieve tasks? 3) How should motor synergies be utilized better? For the first problem we show that goal-directed movements can be achieved by utilizing motor synergies which have properties of achieving the goals. For the second problem, we testify motor synergies which have different properties and discover that energy efficiency is an important aspect in motor synergies which can also be utilized to achieve goal-directed movements. This discovery also implies that we can obtain motor synergies by other ways rather than from goal-directed optimal control signals. For the third problem, we show that the control complexity can be further reduced by utilizing a subset of motor synergies which are effective to achieve goals.