CARF: Contrastive Attraction-Repulsion of Failure-Guided Flow Matching

2026-09-18

AI Summary

CARF通过区分并利用成功与失败轨迹中的关键行为，提出了一种基于对比吸引-排斥的框架来从不完美机器人数据中学习。

Abstract

Robot demonstration collection often produces imperfect or failed trajectories in addition to successful demonstrations. Existing methods typically exploit failed trajectories by identifying segments that still make progress toward task completion, but largely overlook \textit{failure-critical behaviors} that directly lead to task failure. Here we argue that these two types of segments provide fundamentally asymmetric supervision: progressive segments should be imitated, whereas failure-critical segments should be explicitly avoided. Based on this observation, we propose CARF, a Contrastive Attraction-Repulsion of Failure-guided framework for learning from imperfect robot data. CARF introduces a progress-based importance scorer, trained solely on successful expert demonstrations and its perturbation results, to estimate step-wise contributions toward task completion and identify informative regions in failed trajectories. These scores guide a unified flow-matching objective that attracts the policy toward progressive behaviors and repels it from failure-critical ones, while excluding ambiguous segments. This enables more comprehensive utilization of imperfect data and avoids unreliable supervision from ambiguous failure segments. Extensive experiments in simulation and the real world demonstrate consistent improvements over competing baselines across diverse failure scenarios, with ablations further validating the effectiveness of the proposed scoring and attraction-repulsion mechanisms. Our website is https://zhao-sq.github.io/carf/#.

Problem

• robot demonstration
• failed trajectories
• failure-critical behaviors

Innovation

• Contrastive Attraction-Repulsion
• Failure-Guided Flow Matching
• Progress-Based Importance Scorer