Artificial intelligence
Synthetic training data
Definition
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.
Also known as: Synthetic data
Updated
Generate examples and their labels
Tremblay and colleagues train object detection using synthetic images with randomized lighting, poses, and textures. Because the scene is generated, its construction can supply training labels without separately hand-annotating every real camera frame.
A robot-perception dataset could similarly render many object arrangements and provide their positions. Tobin and colleagues study simulated visual data for localization and demonstrate the learned detector in real grasping.
Synthetic experience can include actions
Data need not consist only of images. Dynamics-randomization research trains control policies from simulated robot interaction before testing them on a physical arm.
The simulator can provide observations, actions, and outcomes, but those outcomes reflect its model of physics.
More generated data does not remove the reality gap
Synthetic examples can increase variation and reduce some collection costs. Their usefulness still depends on whether they represent features and behavior relevant to the physical task.
Domain randomization deliberately varies simulated properties to support transfer. It does not make simulated evidence equivalent to hardware evaluation. Reports should state which data were generated, how real data were used, and which real tasks were tested before claiming a perception or control improvement.
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.
Pose estimation
Pose estimation determines the position and orientation of an object or robot relative to a reference frame. For a rigid body in three-dimensional space, a full pose has three translational and three rotational degrees of freedom.