Physics Environment Configuration#

Physics environment configuration defines simulation parameters and model file settings in reinforcement learning training. MotrixLab uses MotrixSim as the physics simulation backend.

Supported File Formats#

MJCF (MuJoCo XML format) - Provides rich physics features and simulation configuration

Model File Configuration#

You need to specify model file paths in environment configuration classes:

@registry.envcfg("my-task")
@dataclass
class MyTaskEnvCfg(EnvCfg):
    # Model file path (required)
    model_file: str = "my_model.xml"

    # Simulation time parameters
    sim_dt: float = 0.002      # Simulation time step
    ctrl_dt: float = 0.02      # Control update frequency

    # Episode parameters
    max_episode_seconds: float = 20.0
    reset_noise_scale: float = 0.01

Recommended Directory Structure#

motrix_envs/my_task/
├── __init__.py          # Module initialization
├── cfg.py               # Environment configuration
├── my_model.xml         # Physics model file
└── my_env.py            # Environment implementation

For complex models with many referenced files, it’s recommended to use folder management.

Common Configuration Issues#

File Path Issues#

When using relative paths, ensure paths are relative to the configuration file location
Avoid using hardcoded absolute paths
Check file permissions and accessibility
Ensure all referenced sub-files exist

Time Step Settings#

ctrl_dt should be an integer multiple of sim_dt
sim_dt that is too small will affect simulation performance
ctrl_dt that is too large will affect control precision
Recommend sim_dt between 0.001-0.02 seconds

Simulation Stability#

Avoid excessively large time steps
Set contact parameters reasonably to avoid penetration
Mass and inertia distribution should be reasonable
Joint limits should match actual conditions

Through proper physics environment configuration, you can create accurate and efficient simulation environments for reinforcement learning training.