Sim-to-Real Transfer

Sim-to-Real Transfer
Transferring robot policies or perception models developed in simulation to physical hardware.
Area	Physical AI and robotics
Applications	Research, engineering and systems operation

Sim-to-Real Transfer transferring robot policies or perception models developed in simulation to physical hardware.

The reality gap

A simulator approximates cameras, contact, friction, dynamics and environmental variation. Even a detailed model cannot capture every condition; a policy may perform well in simulation and fail on the real system.

Transfer approaches

Engineers may randomize simulation parameters, calibrate models, use system identification, fine-tune with limited physical data or design robust control interfaces. The appropriate strategy varies between perception, manipulation and mobile navigation.

Evaluation

A responsible comparison reports simulation assumptions and real-world test conditions separately. Relevant outcomes include task completion, collisions, interventions, degraded performance, recovery behavior and reproducibility.

Constraints

Simulation is valuable for development but cannot replace hardware testing, risk assessment or checks of safety-related controls. A successful transfer demonstration is not proof of deployment readiness.

See also

• Domain Randomization
• Robot Learning from Demonstration
• Robot Safety Risk Assessment

Further reading

• Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World — Tobin et al. (arXiv, 2017)
• AI Risk Management Framework (AI RMF 1.0) — NIST