Topic: biological motor control

Adaptation is essential for biological motor control, enabling rapid modifications of control strategies as the environment changes. However, we still lack a unified network-level theory of coordinated motor adaptation. In previous work, we have proposed a multi-area RNN capable of rapid motor adaptation. The network comprises an internal forward model and a planning/control policy network, both of which are continuously modulated by a network that performs contextual inference. The network is meta-trained to maintain proficient control of hand reaches in the face of changing environmental dyn

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