Workflow:ARISE Initiative Robosuite Gymnasium RL Integration
| Knowledge Sources | |
|---|---|
| Domains | Robotics, Reinforcement_Learning, Gymnasium |
| Last Updated | 2026-02-15 06:00 GMT |
Overview
End-to-end process for wrapping a robosuite manipulation environment with the GymWrapper to make it compatible with the Gymnasium API for reinforcement learning training.
Description
This workflow adapts robosuite environments to conform to the OpenAI Gymnasium interface, enabling seamless integration with standard RL libraries such as Stable-Baselines3, OpenAI Baselines, and other Gymnasium-compatible training frameworks. The GymWrapper translates robosuite's dictionary observation space into a flat numpy array, exposes a standard `action_space` (Box), and implements the Gymnasium `step()` return signature with terminated/truncated flags. This allows researchers to leverage robosuite's diverse manipulation tasks and robot models within established RL training pipelines.
Usage
Execute this workflow when you want to train a reinforcement learning agent on a robosuite manipulation task using a Gymnasium-compatible training framework. This is the standard approach for automated policy learning on robosuite benchmarks.
Execution Steps
Step 1: Configure Base Environment
Create the underlying robosuite environment using `robosuite.make()` with appropriate settings for RL training. Disable camera observations if using low-dimensional state input. Enable reward shaping for dense reward signals. Set the control frequency to ensure smooth simulation dynamics.
Key considerations:
- `use_camera_obs=False` for state-based RL (reduces observation dimensionality)
- `has_offscreen_renderer=False` when not using pixel observations (saves memory)
- `reward_shaping=True` provides dense rewards that accelerate RL training
- `has_renderer=True` only if you need on-screen visualization during training
Step 2: Wrap With GymWrapper
Wrap the robosuite environment instance with `GymWrapper` to expose the Gymnasium API. The wrapper flattens the observation dictionary into a single numpy array, constructs `observation_space` and `action_space` as Gymnasium Box spaces, and converts the `step()` return values to the 5-tuple format (observation, reward, terminated, truncated, info).
Key considerations:
- The wrapper selects observation keys based on the environment configuration
- Custom observation keys can be specified to filter which observations are included
- `env.reset(seed=N)` supports seeded resets for reproducibility
Step 3: Run Training Loop
Execute the standard Gymnasium training loop: reset the environment, sample or compute actions from the policy, step the environment, collect transitions (s, a, r, s', done), and update the policy. The loop handles episode termination and truncation according to Gymnasium conventions.
Key considerations:
- `env.action_space.sample()` generates random actions within the valid range
- `terminated` indicates task success or failure; `truncated` indicates time limit reached
- The environment auto-resets on `env.reset()` with randomized initial conditions
- Standard RL libraries can use this wrapped environment directly
Step 4: Evaluate Trained Policy
After training, evaluate the learned policy by running deterministic rollouts (no exploration noise) and collecting success rates and cumulative rewards. Optionally enable the renderer for visual evaluation of the policy behavior.
Key considerations:
- Toggle `has_renderer=True` for visual evaluation
- Use deterministic action selection (no sampling) during evaluation
- Collect metrics over multiple episodes for statistical significance