  OpenSim is a widely used biomechanics simulator with several anatomically
accurate human musculo-skeletal models. While OpenSim provides useful tools to
analyse human movement, it is not fast enough to be routinely used for emerging
research directions, e.g., learning and simulating motor control through deep
neural networks and Reinforcement Learning (RL). We propose a framework for
converting OpenSim models to MuJoCo, the de facto simulator in machine learning
research, which itself lacks accurate musculo-skeletal human models. We show
that with a few simple approximations of anatomical details, an OpenSim model
can be automatically converted to a MuJoCo version that runs up to 600 times
faster. We also demonstrate an approach to computationally optimize MuJoCo
model parameters so that forward simulations of both simulators produce similar
results.
