AI for Prosthetics Challenge - Reinforcement learning with musculoskeletal models

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AI for Prosthetics Challenge - Reinforcement learning with musculoskeletal models

2019

In this competition, you are tasked with developing a controller to enable a physiologically-based human model with a prosthetic leg to walk and run. You are provided with a human musculoskeletal model, a physics-based simulation environment OpenSim where you can synthesize physically and physiologically accurate motion, and datasets of normal gait kinematics. You are scored based on how well your agent adapts to the requested velocity vector changing in real time.

Technical Committee: Machine Learning for Signal Processing

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