人間とVLAのコミュニケーションギャップ：人間が暗黙に残す情報とポリシーが復元できないもの
The Human--VLA Communication Gap: What Humans Leave Implicit and What Policies Cannot Recover
人間が自然にタスクを指示する際の暗黙的な情報の欠落と、VLAポリシーがそれをどう補えないかを「情報ギャップ」と「実行ギャップ」に分けて分析した研究。
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分類: cs.RO
原文アブストラクト
A robot may know how to perform a task yet fail when a human asks for it naturally. We study this human--VLA communication gap by separating an \emph{information gap}, where task semantics, grounding, or event structure remain unresolved, from a residual \emph{execution gap}, where even a fully specified task depends on how it is organized and invoked. To do this, we characterize how people naturally express demonstrated objectives and evaluate controlled variants of the same physical tasks, with detailed analysis on $π_{0.5}$ and consistent trends on $π_0$-FAST and Cosmos3-Edge. Our study reveals that humans increasingly compress instructions as tasks grow, relying more on contextual grounding and implicit execution structure. Current policies are misaligned with this behavior: they tolerate grounding dependence relatively well, but struggle when task semantics or event structure must be reconstructed. Even after the information gap is resolved, execution remains sensitive to command granularity and invocation state. These findings identify concrete targets for human-centered policy learning and interfaces that better bridge natural human objectives to robot behavior. Project website: https://commgap.github.io/.