Abstract
Efficient, physiologically-detailed musculoskeletal simulators and powerful learning algorithms provide new computational tools to tackle the grand challenge of understanding biological motor control. Our winning solution for the first NeurIPS MyoChallenge leverages an approach mirroring human learning and showcases reinforcement and curriculum learning as mechanisms to find motor control policies in complex object manipulation tasks. Analyzing the policy against data from human subjects reveals insights into efficient control of complex biological systems. Overall, our work highlights the new possibilities emerging at the interface of musculoskeletal physics engines, reinforcement learning and neuroscience.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵† co-first authors;
