Artificial intelligence
Domain adaptation
Definition
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.
Updated
Learn across a source and target mismatch
Domain-Adversarial Training of Neural Networks studies learning representations that support a task while reducing distinguishability between source and target domains. Its formulation uses labeled source examples and unlabeled target examples.
For robot perception, the source could be rendered images and the target could be images from a particular physical camera. The task can remain the same even though image appearance changes.
Adaptation also appears in robot control
Peng and colleagues' locomotion framework uses a domain-adaptation stage to adjust behavior through a learned dynamics representation when moving to a real robot. This targets physical behavior rather than only visual appearance.
A description of adaptation should therefore specify what changes: features, model parameters, a dynamics representation, or another part of the system.
Distinguish adaptation from broad variation
Domain randomization broadens training conditions. Adaptation uses information about a target domain to address a mismatch. They can be combined, as in the cited locomotion framework.
The availability of target labels or interactions also matters. A method that needs target demonstrations is different from one using only unlabeled images. Adaptation results should state those requirements instead of implying that transfer occurred without target-domain information.
Sources
Related terms
Domain randomization
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.
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.
Test-time adaptation
Test-time adaptation adjusts a trained model using data encountered during evaluation or deployment. Unlike ordinary fixed-model inference, it updates model parameters or statistics in response to the target data.