RAPT: Model-Predictive OOD Detection and Failure Diagnosis for Sim-to-Real Humanoid Deployment
Researchers introduce RAPT, a lightweight, self-supervised deployment monitor operating at 50Hz that learns nominal execution from simulation and detects out-of-distribution (OOD) states in real-time during sim-to-real transfer for humanoid robots. In simulation, RAPT improves true positive rate by 37% over the strongest baseline at a 0.5% false positive rate, and on hardware, it achieves 89% TPR with 75% semantic failure diagnosis accuracy across 21 categories. The system also provides interpretable, per-dimension predictive deviation signals and post-hoc semantic failure diagnosis using LLM-based reasoning.
Why it matters: This work advances safety and reliability in deploying learned control policies on humanoid robots by enabling high-frequency, accurate OOD detection and interpretable failure diagnosis during real-world operation.
Full story at: arXiv Robotics ↗