Principle:ARISE Initiative Robosuite Domain Randomization Wrapping

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Overview

Wrapper pattern that applies systematic domain randomization to simulation environments for training robust policies that transfer to real-world settings.

Description

Domain Randomization (DR) is a technique for sim-to-real transfer where simulation parameters are randomized during training so that the policy learns to be robust to visual and physical variations. The DomainRandomizationWrapper orchestrates four types of randomization (texture, camera, lighting, dynamics) via specialized modder objects. Randomization can occur on reset (new domain per episode) and/or every N steps (mid-episode variation).

The wrapper architecture enables compositional randomization strategies by allowing independent control over each randomization type. Each modder object manages a specific aspect of the simulation domain:

TextureModder: Randomizes visual appearance of objects, including colors, textures, and material properties
CameraModder: Randomizes camera parameters such as position, orientation, and field of view
LightingModder: Randomizes lighting conditions including intensity, position, and ambient lighting
DynamicsModder: Randomizes physical properties like mass, friction coefficients, and damping

The temporal flexibility of randomization (per-episode vs. per-step) allows practitioners to control the difficulty of the learning problem and the types of invariances the policy learns.

Usage

Use when training RL policies intended for deployment on real robots, where the sim-to-real gap is a concern. Domain randomization is particularly effective when:

The target deployment environment has significant visual or physical variability
Collecting real-world training data is expensive or dangerous
The simulation is accurate enough to capture essential task dynamics
The policy architecture has sufficient capacity to learn robust features

This approach is most beneficial for manipulation tasks, mobile navigation, and other robotics applications where simulation can provide unlimited training data but reality presents unavoidable variations.

Theoretical Basis

Domain randomization was introduced by Tobin et al. (2017) in "Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World" as a solution to the sim-to-real transfer problem. The core insight is that by training across a distribution of simulated environments rather than a single canonical simulation, the policy learns features that are invariant to the randomized parameters.

Key Theoretical Principles:

The fundamental hypothesis is that if the randomization distribution is broad enough to encompass the real world, then the real-world environment becomes just another sample from the training distribution. Mathematically, if we train on environments sampled from distribution P(E) where E represents environment parameters, and the real environment E_real is within the support of P(E), then the policy π(a|s) learned under P(E) should generalize to E_real.

Randomization as Regularization:

Domain randomization acts as a form of regularization by preventing the policy from overfitting to spurious correlations in a single simulation. By exposing the agent to diverse visual and physical conditions, the learning process is forced to discover robust features that capture the essential structure of the task rather than simulation-specific artifacts.

Tradeoffs:

There exists a fundamental tradeoff between the breadth of randomization and training difficulty. Wider randomization distributions improve robustness and real-world transfer but make the learning problem harder because:

The effective state space becomes larger
Reward signals become noisier across varying environments
More samples are needed to learn invariant features

Conversely, narrow randomization is easier to learn but may not cover the real-world variation, leading to poor transfer. Practitioners must tune the randomization ranges based on task complexity, available compute resources, and knowledge of real-world variation.

Temporal Randomization:

The choice between episode-level randomization (randomize_on_reset) and step-level randomization (randomize_every_n_steps) affects what invariances are learned:

Episode-level: Encourages learning policies robust to different environment configurations but allows adaptation within an episode
Step-level: Forces stronger invariance by preventing any adaptation to specific conditions, suitable for highly variable deployment scenarios

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