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This paper introduces the $\lambda$-hold controller, which leverages the equilibrium-point hypothesis to efficiently train musculoskeletal models for human-like motion in high-dimensional action spaces. By maintaining a constant per-muscle EP threshold length during specific gait phases, the controller significantly reduces the frequency of policy queries, allowing for effective exploration and learning. Remarkably, this approach enables a muscle-actuated skeletal model to learn human-like sprinting using only minimal rewards within just one hour of training.
Human-like sprinting can be achieved in under an hour of training using a novel controller that mimics physiological principles.
The massive overactuation in the human musculoskeletal system makes it challenging to train musculoskeletal models to generate human-like motion via reinforcement learning, primarily because exploration in the resulting high-dimensional and redundant action space is extremely inefficient. To address this problem, we propose the $\lambda$-hold controller, inspired by the equilibrium-point (EP) hypothesis, which has been widely supported by extensive evidence from human motor control studies. The policy's control variable is the per-muscle EP threshold length $\lambda$, from which a stretch-reflex recruitment law computes the muscle excitations automatically. Holding each $\lambda$ over an interval of the gait phase also sharply reduces the frequency at which the policy must be queried. Consequently, the controller, to our knowledge for the first time, enables a muscle-actuated skeletal model to learn human-like sprinting using only a minimal reward within an hour of training. The efficient exploration through the proposed $\lambda$-hold controller is not merely an engineering trick but an approach grounded in physiology, bringing together the EP hypothesis, intermittent control, and optimal feedback control. Beyond encapsulating human-like behavior in predictive simulation, this achievement contributes to developing a learnable model of the human motor controller.