SimtoReal

Simtoreal, often written as sim-to-real or sim-to-real transfer, refers to the set of methods and practices used to bridge the gap between simulated environments and the real world when deploying models or controllers trained in simulation. The central problem is the simtoreal gap: differences in physics, sensor noise, actuator dynamics, and timing can cause a policy that performs well in simulation to fail in reality.

Core concepts include domain randomization, which exposes models to a wide variety of visual, physical, and sensory variations during training to improve generalization; system identification and dynamics randomization to better approximate real-world behavior; calibration of simulators to reflect real hardware characteristics; and the use of hybrid or differentiable simulators to provide more accurate feedback during development.

Applications are common in robotics and automation, including robotic manipulation, legged locomotion, drone control, and autonomous vehicles. Researchers assess progress by evaluating transfers from simulated benchmarks to real tasks, noting improvements in transfer success and reductions in the observed performance gap.

Challenges persist in accurately modeling contact dynamics, friction, and unmodeled disturbances; perception gaps between simulated and real sensors; hardware delays and safety concerns during real-world testing; and the generalization of learned policies to new tasks or environments.

Future directions focus on more realistic physics engines, differentiable simulation technologies, improved domain adaptation techniques, and standardized benchmarks to quantify simtoreal transfer performance.

See also: sim-to-real transfer, domain randomization, transfer learning, sim2real robotics.