Here’s a list of the top simulators used for training AI agents to walk, run, or perform complex locomotion—especially in research and robotics. These simulators support physics-based environments and often integrate well with reinforcement learning (RL) and imitation learning workflows.
Top Simulators for AI Locomotion & Walking
| Simulator | Best For | Language/API | Highlights |
|---|---|---|---|
| MuJoCo (Multi-Joint dynamics with Contact) | Advanced RL, humanoid locomotion | Python/C++ | Physics-accurate, used in OpenAI & DeepMind papers |
| Isaac Gym | High-performance RL on GPU | Python (PyTorch) | Very fast, supports thousands of parallel agents |
| Unity ML-Agents | Game-like 3D environments, visuals | C#/Python | Visual + physics-rich, easy integration with video |
| PyBullet | Robotics, fast prototyping | Python | Free & beginner-friendly, real-time physics |
| NVIDIA Omniverse Isaac Sim | Robotics simulation with photorealism | Python | ROS2 + Isaac SDK compatible, very detailed |
| Webots | Mobile robots & multi-agent sim | Python/C++/ROS | Educational + commercial robotics support |
| Gazebo (Ignition) | ROS-based robots in physics worlds | C++/Python | Strong ROS integration, used in real robot deployment |
| DeepMind Control Suite (DMC) | RL locomotion research | Python (dm_control) | Lightweight and deterministic, works with JAX |
| Brax (by Google) | Fast physics with JAX | Python (JAX) | Ultra-fast, 100% differentiable physics |
| OpenSim | Biomechanical simulations | MATLAB/Python | Human musculoskeletal modeling, gait analysis |
| V-REP / CoppeliaSim | Robot simulation and scripting | Python/Lua/C++ | Flexible scripting for complex robot tasks |
Recommendations Based on Use-Case
Beginners:
- PyBullet
- Unity ML-Agents (with low-fidelity models)
RL Research:
- MuJoCo (used in OpenAI’s DeepMimic, OpenAI Gym)
- Isaac Gym
- DeepMind Control Suite
- Brax
Real Robot Control & ROS:
- Gazebo
- Webots
- Omniverse Isaac Sim
Human Motion Modeling:
- OpenSim
- MuJoCo + Human3.6M or AMASS datasets