Topic: static-to-dynamic stability

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. The training procedure, which we call static-to-dynamic stability (SDS), defines a curriculum of progressively more complex state-space neighborhoods, which guid