失敗境界学習によるロバストな視覚言語行動モデル
Where Success Breaks: Failure-Boundary Learning for Robust Vision-Language-Action Models
専門家デモの模倣だけでは失敗境界が不明なため、デジタルツインでのロールアウトとセマンティック進捗定位を用いて失敗境界を発見・整形し、ロバストなVLAモデルを実現する手法DLSを提案。
著者: Yanzhe Chen, Zhijun Cao, Mike Zheng Shou
分類: cs.RO
原文アブストラクト
Vision-language-action (VLA) models adapted through supervised fine-tuning (SFT) inherit a structural asymmetry: expert demonstrations teach the policy where success behavior lies, but provide no signal about where it ceases to be reliable. We argue that robust VLA adaptation should therefore be viewed not as further demonstration fitting, but as **Failure-Boundary Learning**---the problem of *Discovering*, *Localizing*, and *Shaping* the boundary between recoverable deviations and task failure. To instantiate this view, we propose **DLS**: built on a **real-grounded behavioral prior** from few real demonstrations and simulated co-training, DLS *discovers* failure boundaries at scale through on-policy digital twin rollouts. Rather than reducing each rollout to a binary label, **semantic progress localization** uses privileged simulator states to assign progress-aware signals that capture *where* the failure boundary is crossed, not merely *whether*. These signals drive **directional boundary shaping** in the flow dynamics---reinforcing success-producing denoising directions and suppressing failure-producing ones, without action likelihoods or auxiliary critics. Across real-robot manipulation tasks, DLS improves robustness over SFT and online RL baselines, especially under randomized initial states and unseen visual conditions.