タスクプロトタイプ誘導フローマッチングによる視覚言語ロボット操作の少数ショット汎化
Task-Prototype Guided Flow Matching for Few-Shot Generalization in Vision-Language Robot Manipulation
少数の実演からタスクの位相レベルのプロトタイプを抽出し、フローマッチングの初期分布と速度場を誘導することで、視覚言語ロボット操作を新しい手順に少数ショットで適応させる手法を提案。
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著者: Yizhao Wang, Guantao Zhang, Jingbo Wang
分類: cs.RO, cs.CV
原文アブストラクト
Vision-language robot manipulation policies can follow semantic instructions, but adapting them to a new procedure from only a few demonstrations remains difficult because language underspecifies contact timing, motion phases, corrective behavior, and execution style. This paper presents Task-Prototype Guided Flow Matching (TP-Flow), a few-shot manipulation framework that converts support demonstrations into structured task-prototype tokens and uses them to guide both the initial flow prior and the velocity field. TP-Flow employs symmetric cross-attention with learnable queries to extract phase-level prototypes, parameterizes a task-adaptive initial distribution, and injects prototype information through gated adaptive normalization. It is trained with an episodic support-query objective and prototype contrastive regularization, so few-shot adaptation is simulated during training while nuisance information is suppressed. On the LEROBOT-ARM-SO101 platform, TP-Flow achieves 66.8\%, 79.6\%, and 82.1\% success rates under 1-, 4-, and 6-shot settings, with a 75.5\% few-shot AUC. At 1-shot, it improves over CFM, Pooled-Demo CFM, and In-Context Flow by 29.8, 14.5, and 9.9 percentage points. It also improves held-out target-group generalization across novel-object transfer, goal recombination, long-horizon composition, and contact/correction tasks. TP-Flow maintains real-time execution with six online prototype tokens, 54.3 ms latency, 3.9 GB peak memory, and a 10 Hz control rate, while reducing the noisy-support success drop to 6.2\%. Theoretical diagnostics show that prototype distance aligns with action-distribution distance, the adaptive prior reduces transport cost, and gated modulation keeps measured trajectory deviations below the derived ODE bound. The code repository is omitted for anonymous review.