Reference
Humanoid Robotics Glossary
Clear, source-backed definitions of humanoid robotics, embodied AI, robot learning, training data, control, and simulation terms.
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Simulation & transfer
Digital twin
A digital twin is a fit-for-purpose digital representation of a specific physical robot, asset or process that is kept synchronised with its real counterpart through operational data. It may contain geometry, dynamics and simulation models, but the maintained link to an identified real system distinguishes it from an ordinary, standalone simulator.
Simulation & transfer
Domain randomisation
Domain randomisation is a simulation-training technique that varies selected visual, physical or sensor parameters across generated examples or episodes. By exposing a model or policy to a distribution of environments rather than one calibrated scene, it aims to make real conditions fall within the learned variation and improve sim-to-real transfer.
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Simulation & transfer
Sim-to-real
Sim-to-real is the transfer of a model, policy or behaviour developed wholly or partly in simulation to a physical robot or real environment. The central problem is the reality gap: errors in simulated dynamics, sensing, appearance and timing can make a strategy successful in simulation but unreliable or unsafe on hardware.
Simulation & transfer
Synthetic data
Synthetic data is training or evaluation data produced computationally rather than measured directly from the target real-world event. In robotics it can include rendered sensor observations, automatically generated labels, reconstructed or generated scenes, and simulated trajectories. A simulated rollout is one subtype: a temporally linked trajectory produced by executing a controller or policy in a simulator.