Overview
Sim-to-real transfer addresses the difference between a simulated environment and a physical robot. Domain randomization varies aspects of simulation during training so models encounter a wider range of conditions. Tobin and colleagues studied this approach for transferring visual models to the real world. Successful transfer on a documented task does not establish reliability across all environments. Evaluation should record the hardware, task, training setup and deployment conditions.[1]
References
- Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real WorldTobin et al. · Academic paper · Publication date not stated · Accessed 2026-10-04↑ Return to article