Black-box correction of deployed robot policies with world-model planningExpert
Back on Track: Deployment-time Policy Improvement via World Model Planning
Robot AI models are increasingly sold as closed systems that buyers cannot retrain. This Stanford preprint wraps such a model with a small watchdog and planner that step in only when a task stalls, lifting pi0.5 from 72.6% to 81.5% on a perturbed benchmark and a real Franka arm from 40% to 60% on a lunchbox task.