Artificial intelligence
Domain randomization
Definition
Domain randomization varies properties of training environments to encourage a learned model or policy to work across changing conditions. In robotics it often randomizes simulated appearance, physical parameters, or both to support transfer to real hardware.
Also known as: Domain randomisation
Updated
Vary appearance or physical behavior
The visual domain-randomization study trains object localization using rendered scenes with varied appearance, including nonrealistic random textures. The aim is for a real camera image to resemble another variation within the training experience.
Dynamics randomization applies the same broad principle to simulated physical behavior. The authors use it to train a robot-arm pushing policy that transfers to hardware.
Randomization should match the transfer problem
For a visual picking system, changes in textures and lighting target perception differences. For a control policy, variation in dynamics targets differences in how actions move the robot and objects. These choices address different parts of the system.
Randomization can therefore be useful without producing photorealistic images. Equally, varied images alone do not model an actuator's physical response.
Variation is not a transfer guarantee
The cited studies demonstrate particular sim-to-real transfers. Their results do not show that arbitrary randomization covers every real operating condition.
Domain adaptation is a neighboring concept: it uses information about a target domain to reduce a mismatch. Domain randomization generally broadens training variation. A robotics pipeline may combine them, but neither term should be used as evidence that a new environment has already been validated.
Sources
Related terms
Sim-to-real transfer
Sim-to-real transfer applies a model, policy, or behavior developed in simulation to a physical system. Its central challenge is the difference between the simulated environment and the robot, sensors, and interactions encountered in reality.
Synthetic training data
Synthetic training data is data produced computationally for model training rather than collected directly as the corresponding real-world examples. In robotics, it often includes rendered sensor observations, simulated trajectories, and labels available from the simulator.
Domain adaptation
Domain adaptation adjusts a learned model to work on a target data distribution that differs from its source training distribution. Robotics examples include adapting perception from simulation to camera images or adapting behavior to changed physical conditions.