Reinforcement learning with musculoskeletal models

NIPS 2018: AI for prosthetics

Design artificial intelligence to control human body and predict performence of a prosthetic leg. Participate in the NIPS 2018 challenge to win prizes and fame.

Learn more about the challenge →

OpenSim RL

Use our musculoskeletal reinforcement learning environment for other projects in computer science, neuroscience, biomechanics, etc.

Learn more about osim-rl →

Get up and running in seconds.

Quick-start Instructions

~ $ conda create -n opensim-rl -c kidzik opensim python=3.6.1

~ $ source activate opensim-rl

~(opensim-rl) $ conda install -c conda-forge lapack git

~(opensim-rl) $ pip install git+https://github.com/stanfordnmbl/osim-rl.git

~(opensim-rl) $ python

from osim.env import L2RunEnv
env = L2RunEnv(visualize=True)
observation = env.reset(difficulty = 0)
for i in range(200):
    o, r, d, i = env.step(env.action_space.sample())

NIPS 2017: Learning to Run challenge

In 2017 we used osim-rl in a challenge at NIPS were participants were asked to build controllers for running. They did great :)