Topic: policy gradient

Building realistic models for complex and skilful motor tasks such as dexterous object manipulation is hampered by two key challenges: the need for physiologically-detailed musculoskeletal models and powerful algorithms that are able to tackle such high-dimensional problems. The newly released MyoSuite ecosystem provides such musculoskeletal models and is 4000x faster than previous simulators, thereby enabling us to develop and test algorithms for agile object manipulation. Combining ideas from stochastic optimal control and reinforcement learning (RL), we develop a training curriculum called

The ease with which two-legged dogs can run illustrates the versatility and agility of mammals in learning arbitrary motor skills, an ability currently unrivaled by artificial systems. How the brain supports these capacities is unclear, as control mechanisms are distributed and interdependent between many brain regions. We make progress on this question by developing a neural network model comprising three major brain systems that are known to play central roles in learned motor control: the cerebellum (CB), primary motor cortex (M1), and associative sensorimotor cortex (ASC). We both leverage