BMI Thesis Prize 2025 Seminar // Alberto Chiappa: Musculoskeletal motor control with reinforcement learning

Animals, including humans, interact with the external environment primarily through motion. Replicating their motor control skills in artificial embodied agents is a major objective of artificial intelligence research. Using biologically realistic computational models of the human musculoskeletal system, we can study motor skill learning and adaptation in simulation with unprecedented detail and efficiency. Advanced biomechanical simulators allow us to train policies that have to deal with the complexity and high-dimensionality of biological motor control. This talk presents a collection of studies focused on different aspects of artificial embodied intelligence, linked by one common underlying research question: the learnability of human-level motor control policies.