micropilot/myosuite

MyoSuite is a research toolkit for simulating musculoskeletal models, meaning digital models of muscles, bones, and joints, and using them to study how movement and motor control work. It runs on a physics engine called MuJoCo, which simulates realistic physical motion, and wraps its simulated environments in the OpenAI gym interface, a widely used standard for reinforcement learning, a branch of machine learning where an AI learns by trial and error to control something, in this case a simulated body. The project provides a large collection of ready made tasks and environments, things like controlling a simulated elbow or other joints, that researchers can use to train and test machine learning agents on biomechanical control problems, meaning teaching software to move a simulated body the way a nervous system controls a real one. Installing it requires Python 3.10 or newer, and the README offers a few paths: using a fast package manager called uv, using conda, a common tool for managing Python environments, or installing from source by cloning the repository directly. Once installed, a test command lists all available simulated environments, and a separate command lets you visually watch an environment run with random actions. Using MyoSuite in code looks like using any other OpenAI gym environment: you create an environment by name, reset it, then repeatedly render it and step through actions. The README links to written tutorials and to Colab notebooks, meaning browser based interactive Python notebooks, that walk through loading models, training them, and visualizing the results, plus a folder of pre-trained example policies for testing. The project is licensed under the Apache License, and it originates from published academic research on musculoskeletal motor control simulation.