Principle:ARISE Initiative Robosuite Visual Feedback Wrapping
Metadata:
- robosuite
- Robotics_Simulation
- Teleoperation
- last_updated: 2026-02-15 12:00 GMT
Overview
Decorator pattern for augmenting simulation environments with visual indicator overlays to provide real-time feedback during teleoperation.
Description
The Wrapper/Decorator pattern in robosuite allows adding capabilities to environments without modifying the base class. The VisualizationWrapper adds visual indicator sites (spheres, markers) to the MuJoCo model that show target positions or goals during teleoperation. It performs a hard reset on initialization to inject indicator geometry into the MuJoCo model XML.
This pattern enables modular enhancement of environment capabilities by wrapping environments in layers, where each wrapper adds specific functionality while delegating core environment operations to the wrapped instance.
Usage
Use when building teleoperation or interactive control interfaces where visual goal indicators help the operator. The wrapper is particularly useful in demonstration collection pipelines where human operators need clear visual feedback about target positions or desired end-effector locations.
Theoretical Basis
The Decorator/Wrapper design pattern is a structural design pattern that allows behavior to be added to individual objects dynamically without affecting the behavior of other objects from the same class. In the context of robosuite environments:
- The wrapper inherits from a base Wrapper class that defines the interface
- The wrapper delegates all standard environment methods (step, reset, render, etc.) to the wrapped environment
- The wrapper augments specific methods or adds new capabilities (in this case, visual indicators)
Key characteristics:
- Transparency: The wrapper exposes the same interface as the wrapped object
- Composability: Multiple wrappers can be chained together
- Single Responsibility: Each wrapper adds one cohesive piece of functionality
Pseudocode for wrapping chain:
# Base environment
base_env = RobotEnv(...)
# Apply visualization wrapper
env = VisualizationWrapper(base_env, indicator_configs='default')
# Optional: Chain additional wrappers
env = DataCollectionWrapper(env)
env = DomainRandomizationWrapper(env)
# The wrapped environment maintains the same interface
obs = env.reset()
obs, reward, done, info = env.step(action)
env.render()
# But with added visualization capability
env.update_indicator(position=[0.5, 0.0, 0.8])
Implementation pattern:
class VisualizationWrapper(Wrapper):
def __init__(self, env, indicator_configs=None):
super().__init__(env)
# Add visual indicators to the model
self._setup_indicators(indicator_configs)
# Hard reset to inject indicator geometry
self.env.reset()
def reset(self):
# Delegate to wrapped environment
return self.env.reset()
def step(self, action):
# Delegate to wrapped environment
return self.env.step(action)
def update_indicator(self, position):
# New capability added by wrapper
self.sim.data.set_mocap_pos('indicator', position)