RACE: Rollout-Free Calibrated Cumulative Scoring for Robot Policy Checkpoint Selection

The study proposes RACE, a robot policy checkpoint scoring metric that requires no real-world rollout. It uses a fixed set of held-out successful trajectories to calibrate action deviations against state-conditional thresholds, then applies a running product to reduce the weight of later reference predictions after large deviations. In simulation, RACE outperformed baselines across different validation sources, while some baselines degraded noticeably when the validation set changed; on a real robot, its rankings were consistent with deployment success rates across the evaluated policy backbones and tasks of varying difficulty.