Tootfinder

SPiDR: A Simple Approach for Zero-Shot Safety in Sim-to-Real Transfer
Yarden As, Chengrui Qu, Benjamin Unger, Dongho Kang, Max van der Hart, Laixi Shi, Stelian Coros, Adam Wierman, Andreas Krause
https://arxiv.org/abs/2509.18648

SPiDR: A Simple Approach for Zero-Shot Safety in Sim-to-Real Transfer
Safety remains a major concern for deploying reinforcement learning (RL) in real-world applications. Simulators provide safe, scalable training environments, but the inevitable sim-to-real gap introduces additional safety concerns, as policies must satisfy constraints in real-world conditions that differ from simulation. To address this challenge, robust safe RL techniques offer principled methods, but are often incompatible with standard scalable training pipelines. In contrast, domain randomi…