# PRIME: Learning Precise Progress Estimation from Imprecise Demonstrations

- Source: arXiv cs.RO
- Published: 2026-10-19T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/y89fp5ipyfg2ligf95l87z9ws
- Original: https://arxiv.org/abs/2610.23769

## Summary

The paper proposes PRIME, a progress estimator for generating dense reward signals in reinforcement learning for robotic manipulation, learning precise task progress estimation from imprecise demonstration data. PRIME builds a weighted graph over observed states, connecting states across trajectories based on spatial proximity and visual consistency; edge weights encode spatial distance as transition cost, and shortest-path search approximates the geodesic distance to success, whose changes can distinguish progress, stagnation, and regression without assuming that progress is monotonic over time.