外观
论文arXiv 2610.2376912 天前
并非所有动作贡献相同：从不精确示范中学习精确进度估计
原文链接：Not All Actions Contribute Equally: Learning Precise Progress Estimation from Imprecise Demonstrations（arXiv 2610.23769 · 发布日期：2026-10-16）
Not All Actions Contribute Equally: Learning Precise Progress Estimation from Imprecise Demonstrations
针对示范中动作贡献不均的问题，PRIME用跨轨迹图和最短路学习进度估计，并在仿真与真实精密操作中验证。
核心要点
- 现有进度估计依赖专家数据，常把人为失误误判为有效进展。
- PRIME在观测状态上构建加权图，按空间接近和视觉一致性连接跨轨迹状态。
- 仿真和真实精密操作实验显示，其在多样任务和不同次优数据下稳定有效。
- 高精度场景优势更明显，在充电器插入上相对SFT基线成功率最高提升5倍。
验证范围与局限：验证范围为仿真和真实世界中的精密操作任务。
英文摘要 (Abstract)
Progress estimators play an essential role in providing dense reward signals for reinforcement learning in robotic manipulation, but accurately assessing task progress can be much challenging. Existing methods heavily rely on expert data and naively assume that all actions within a demonstration contribute to task success. This assumption often breaks down in real-world scenarios, especially in high-precision manipulation, where subtle geometric errors can lead to task failure. Therefore, these methods can erroneously interpret human mistakes as meaningful progress, further corrupting progress learning. We argue that a key limitation is the lack of a global prior regarding task evaluation criteria, which is crucial for distinguishing meaningful progress from human mistakes. To this end, we propose PRIME, a novel progress estimator capable of learning precise progress estimation from imprecise demonstrations by aggregating experience across trajectories. PRIME constructs a weighted graph over observed states, connecting states across trajectories based on spatial proximity and visual consistency. Edge weights encode spatial distances as transition costs, and shortest-path search approximates the geodesic distance to success. Changes in this distance naturally distinguish advancement, stagnation, and setbacks without assuming monotonic progress over time. Extensive experiments in both simulation and real world on precise manipulation tasks show that PRIME is consistently effective across diverse tasks, and robust to data with varying degrees of suboptimality. Notably, the advantage of PRIME gets more pronounced in higher-precision scenarios, yielding up to a (5\times) improvement in policy success rate over the SFT baselines on charger insertion.
作者：Huanqian Wang, Boshi Tang, Botong Xiao, Jie Zhang, Yang Yue, Danhua Tao, Shiji Song, Gao Huang
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