ロボット行動表現のための意味的アンカリング
Semantic Anchoring for Robotic Action Representations
VLAモデルの微調整で失われる意味構造を保つため、行動表現を意味多様体に固定するプラグアンドプレイ手法を提案し、実世界で最大+18.7%の成功率向上を達成した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
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著者: Yuan Xu, Youheng Shi, Chengyang Li, Wentao Zhu, Yizhou Wang
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
Vision-Language-Action (VLA) models inherit rich semantic representations from pretrained Vision-Language Models, yet fine-tuning on limited robot demonstrations degrades this structure and undermines generalization. A fundamental question therefore arises: what constitutes a good action representation? Inspired by the mirror neuron theory's insight that observation and execution share an intention-level encoding, we examine whether a robot's action representations preserve the semantic structure captured by pretrained encoders. Systematic probing confirms that this structure erodes during finetuning, and that its quality synchronizes with both task success and out-of-distribution generalization. We further introduce a plug-and-play method that anchors action representations to a semantic manifold while decomposing representations into a shared semantic channel and a private channel, all discarded at inference, leaving the deployed model unchanged. Validated on different VLA backbones across simulation and real-world benchmarks, our method yields up to +18.7% on real-world in-distribution tasks and +21.5% on out-of-distribution generalization.
