Sim-to-real reliability of learning-based methods

Determine whether learning-based methods developed and evaluated in simulation reliably transfer to real-world robotic systems, and characterize the conditions that affect successful sim-to-real transfer.

Background

Simulation enables scalable evaluation and training, but domain gaps—especially in sensing and contact dynamics—can undermine real-world performance.

The authors note persistent uncertainty about whether methods validated in simulation will work in the real world, underscoring the need to understand and mitigate sim-to-real gaps.