CAC-VLA: 視覚言語行動モデルのための文脈ゲート型行動条件付け
CAC-VLA: Context-Gated Action Conditioning for Vision-Language-Action Models
VLAモデル内に軽量な潜在行動インターフェースを導入し、文脈ゲートで行動エキスパートへの影響を調整することで、ロボット操作の成功率を向上させた。
詳しい要約
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
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著者: Yifu Xiong, Wenhao Yu, Jiaxuan Lin, Bojun Zou, Jiahao Li, Lu Zhang, Yanyong Zhang, Jianmin Ji
分類: cs.RO
原文アブストラクト
Vision-Language-Action (VLA) models have become a promising paradigm for generalist robot manipulation, where visual-language representations are used to condition continuous action generation. However, these representations are not explicitly optimized for action conditioning, leaving the action expert to bridge the gap between multimodal understanding and precise motor control. Recent action-reasoning methods introduce additional modules to generate explicit action plans or action-space reasoning signals, demonstrating the benefit of action-level guidance but often requiring separate action-generation frameworks. We propose CAC-VLA, a Context-Gated Action Conditioning framework that learns a lightweight latent-action interface directly within the VLM. Instead of generating executable trajectories, CAC-VLA trains the VLM to predict coarse-to-fine latent actions, which are structured representations encoded from future action segments, and adaptively leverages them to condition the action expert via a context gate. This enables VLM-native action conditioning while calibrating the influence of latent-action guidance on expert action generation. Experiments on LIBERO and LIBERO-Plus demonstrate the effectiveness of CAC-VLA, achieving 98.3% average success rate on LIBERO and 89.5% LIBERO-Plus, suggesting that context-gated latent-action conditioning is an effective interface for continuous expert control.
