SaPaVe: ロボットの視覚言語行動モデルにおける能動的知覚と操作の実現
SaPaVe: Towards Active Perception and Manipulation in Vision-Language-Action Models for Robotics
能動的知覚と操作を統合するエンドツーエンドフレームワークSaPaVeを提案し、カメラと操作の行動を分離しつつ協調学習することで、動的視点下でのロバストな能動的操作を実現した。
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？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Mengzhen Liu, Enshen Zhou, Cheng Chi, Yi Han, Shanyu Rong, Liming Chen, Pengwei Wang, Zhongyuan Wang, Shanghang Zhang
分類: cs.RO, cs.CV
原文アブストラクト
Active perception and manipulation are crucial for robots to interact with complex scenes. Existing methods struggle to unify semantic-driven active perception with robust, viewpoint-invariant execution. We propose SaPaVe, an end-to-end framework that jointly learns these capabilities in a data-efficient manner. Our approach decouples camera and manipulation actions rather than placing them in a shared action space, and follows a bottom-up training strategy: we first train semantic camera control on a large-scale dataset, then jointly optimize both action types using hybrid data. To support this framework, we introduce ActiveViewPose-200K, a dataset of 200k image-language-camera movement pairs for semantic camera movement learning, and a 3D geometry-aware module that improves execution robustness under dynamic viewpoints. We also present ActiveManip-Bench, the first benchmark for evaluating active manipulation beyond fixed-view settings. Extensive experiments in both simulation and real-world environments show that SaPaVe outperforms recent vision-language-action models such as GR00T N1 and \(\pi_0\), achieving up to 31.25\% higher success rates in real-world tasks. These results show that tightly coupled perception and execution, when trained with decoupled yet coordinated strategies, enable efficient and generalizable active manipulation. Project page: https://lmzpai.github.io/SaPaVe
