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Neural control is an exciting mystery which we instinctively master. Yet,researchers have a hard time explaining the motor control trajectories.Physiologically accurate biomechanical simulations can, to some extent, mimiclive subjects and help us form evidence-based hypotheses. In these simulatedenvironments, muscle excitations are typically calculated through inversedynamic optimizations which do not possess a closed-form solution. Thus,computationally expensive, and occasionally unstable, iterative numericalsolvers are the only widely utilized solution. In this work, we introduceArtiSynth, a 3D modeling platform that supports the combined simulation ofmulti-body and finite element models, and extended to support reinforcementlearning (RL) training. we further use ArtiSynth to investigate whether a deepRL policy can be trained to drive the motor control of a physiologicallyaccurate biomechanical model in a large continuous action space. We run acomprehensive evaluation of its performance and compare the results with theforward dynamics assisted tracking with a quadratic objective function. Weassess the two approaches in terms of correctness, stability,energy-efficiency, and temporal consistency.

Reinforcement Learning for High-dimensional Continuous Control in Biomechanics: An Intro to ArtiSynth-RL