聞くことが容易になるとき：視覚的手がかりを除去してショートカットのないVLAを実現
When Listening Becomes Easier: Scrubbing Visual Cues for Shortcut-Free VLAs
視覚言語行動モデルにおける視覚的ショートカット学習を分析し、言語情報への注意を高める新しいドメイン敵対的訓練手法「タスクスクラビング」を提案して、分布外ロバスト性を改善した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Jasper Gerigk, Kenzo Aspuru-Takata, Chin-Hsuan Wu, Mohammad Mohammadi, Shuhong Zheng, Igor Gilitschenski
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
Shortcut learning is a prevalent issue in robot learning. The limited diversity of robot demonstration datasets can mislead policies into exploiting spurious correlations between tasks and irrelevant features, such as viewpoint or background. Collecting sufficiently diverse robot demonstrations is costly and inefficient, motivating algorithmic alternatives. We focus on vision-language-action (VLA) models and discover that different vision-language model backbones exhibit substantially different levels of susceptibility to visual shortcut learning. We find that model behavior correlates with our proposed representation-level metric, action margin, which requires no policy rollouts. Visual shortcuts consistently enter action representations in early layers, with models differing in the extent to which later layers correct them by incorporating language information. To boost models' attention to language, we introduce task scrubbing, a new domain-adversarial training method that decreases models' likelihood of using visual shortcuts and improves VLAs' generalization. Experiments in both simulation and the real world across multiple VLAs and visual cues show that task scrubbing improves out-of-distribution robustness and often eliminates visual shortcut learning.