Workflow:ARISE Initiative Robosuite Domain Randomization Training
| Knowledge Sources | |
|---|---|
| Domains | Robotics, Sim_To_Real, Domain_Randomization |
| Last Updated | 2026-02-15 06:00 GMT |
Overview
End-to-end process for configuring and applying domain randomization to robosuite environments for training robust policies that transfer from simulation to real-world robots.
Description
This workflow sets up the domain randomization infrastructure in robosuite to train policies that are robust to visual and physical variations encountered in real-world deployment. The DomainRandomizationWrapper applies configurable randomization modders (TextureModder, CameraModder, LightingModder, DynamicsModder) that procedurally alter textures, camera parameters, lighting conditions, and physical dynamics at each episode reset. Instance randomization groups related geometry elements so they are randomized consistently. The resulting policies learn to be invariant to these variations, improving sim-to-real transfer.
Usage
Execute this workflow when you are training a policy in simulation that will be deployed on a real robot and you need the policy to be robust to visual and physical differences between simulation and the real world. This is the standard approach for sim-to-real transfer research using robosuite.
Execution Steps
Step 1: Configure Base Environment
Create the robosuite environment with the desired task and robot configuration. Enable offscreen rendering if training with visual observations. Set global macros for instance randomization mode, which ensures that related geometric groups (e.g., all parts of a robot arm) are randomized together for visual consistency.
Key considerations:
- Set `macros.USING_INSTANCE_RANDOMIZATION = True` for grouped randomization
- Instance randomization ensures all geoms in a body group share the same random texture
- Without instance randomization, each geom is randomized independently (less realistic)
Step 2: Wrap With DomainRandomizationWrapper
Apply the `DomainRandomizationWrapper` around the base environment. Configure which randomization channels to enable: color/texture randomization, camera viewpoint randomization, lighting randomization, and dynamics randomization. Set the randomization frequency (per-step, per-reset, or at a fixed interval).
Key considerations:
- `randomize_color=True` applies random textures to object surfaces
- `randomize_camera=True` perturbs camera position and orientation
- `randomize_lighting=True` varies light positions, intensity, and color
- `randomize_dynamics=True` modifies physical properties (friction, mass, damping)
- Randomization interval controls how often new random parameters are sampled
Step 3: Configure Randomization Parameters
Fine-tune the randomization modders to control the range and distribution of each randomization channel. The TextureModder supports several modes (noise, gradient, checker patterns). CameraModder perturbs camera poses within configurable bounds. DynamicsModder modifies inertial properties, friction coefficients, and joint damping.
Key considerations:
- TextureModder can randomize specific geom groups (robot, object, environment)
- CameraModder bounds should be set to avoid unrealistic viewpoints
- DynamicsModder changes take effect via MuJoCo model recompilation
- Overly aggressive randomization can make the task unsolvable
Step 4: Train With Randomized Environments
Run the RL or imitation learning training loop using the randomized environment. At each episode reset (or at the configured interval), the wrapper automatically applies new random parameters. The training algorithm sees diverse visual and physical conditions, encouraging the policy to learn invariant representations.
Key considerations:
- The environment can be additionally wrapped with GymWrapper for RL compatibility
- Training with randomization typically requires more episodes than without
- Monitor training metrics to detect if randomization is too aggressive
- Visual randomization is most impactful for vision-based policies
Step 5: Evaluate Transfer Performance
Evaluate the trained policy under both nominal (no randomization) and randomized conditions. Compare performance to a baseline policy trained without randomization. For real-world deployment, test the policy on the physical robot to measure actual sim-to-real transfer gap.
Key considerations:
- Evaluate with randomization disabled to measure nominal performance
- Evaluate with varying randomization levels to assess robustness
- The real-world performance gap is the ultimate metric for sim-to-real approaches