MyoHub/myosuite

Health v2 · maintenance only

Adoption not part of the score

1210 stars · 173 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

MyoSuite is a collection of musculoskeletal environments and tasks simulated with the MuJoCo physics engine and wrapped in the OpenAI gym API. It enables applying machine learning and reinforcement learning to biomechanical motor control problems such as dexterity, manipulation, and locomotion.

Use cases

• train reinforcement learning agents to control musculoskeletal models
• simulate human dexterity and in-hand manipulation tasks
• research motor control and biomechanics with MuJoCo
• benchmark policies on contact-rich musculoskeletal environments
• study locomotion and postural control with gym-style APIs
• teach neuromechanics and motor control with tutorials

When to choose

• you need gym-compatible environments with physiologically realistic musculoskeletal models
• you want to apply RL or ML to biomechanical control problems
• you need contact-rich simulation of muscles, tendons, and joints in MuJoCo

When to avoid

• you need rigid-body robot arm manipulation without muscle models
• you need real-time interactive simulation or game physics
• you need a lightweight environment without MuJoCo dependencies

Facets

library · maturity active

machine-learning simulation reinforcement-learning testing machine-learning robotics simulation artificial-intelligence python windows cross-platform mujoco musculoskeletal gym motor-control biomechanics reinforcement-learning-environments research linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="MyoHub/myosuite")