chore: release v0.0.1

docs/source/en/user_guide/tutorial/physics_environment.md

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+# Physics Environment Configuration
+
+Physics environment configuration defines simulation parameters and model file settings in reinforcement learning training.
+MotrixLab uses [MotrixSim](https://motrixsim.readthedocs.io/en/latest/user_guide/index.html) as the physics simulation backend.
+
+## Supported File Formats
+
+-   [**MJCF**](https://mujoco.readthedocs.io/en/stable/XMLreference.html) (MuJoCo XML format) - Provides rich physics features and simulation configuration
+
+## Model File Configuration
+
+You need to specify model file paths in environment configuration classes:
+
+```python
+
+@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.