Gnomon4D: 幾何学的動作に基づくワールドアクションモデリングのための注目領域学習
Gnomon4D: Learning Where and When to Focus for Geometric Motion-Grounded World-Action Modeling
未来の映像・ロボット動作・4D幾何学的動作場(GeoDelta)を同時生成するワールドアクションモデルを提案し、人間とロボットの動作関係を共有空間で捉えて行動事前学習を実現した。
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著者: Lingwei Dang, Yan Zhou, Juntong Li, Zhenhao Zhang, Yiteng Chen, Xiaoqiang Liu, Liang An, Hongwen Zhang, Wei Min, Guoxin Zhang, Pengfei Wan, Yebin Liu, Qingyao Wu
分類: cs.CV
原文アブストラクト
World-action models (WAMs) use video-generation priors to jointly predict future observations and robot actions. Yet RGB futures entangle physical state changes with appearance, while frame-wise depth and point maps lack temporal correspondence. Existing 4D approaches either keep motion implicit in visual features or translate predicted traces into actions through a separate interface. We present Gnomon4D, a WAM that jointly generates future video, robot actions, and GeoDelta, a video-aligned 4D geometric motion field encoding the cross-frame image-plane displacement and depth change of embodiment and object points. By placing embodiment motion and object responses in a shared space, GeoDelta exposes their motion relations to the action expert at inference. Interaction-Salient Attention (InSAttn) uses these relations to focus spatial readout on task-relevant interaction regions, while Transition-Anchored Aggregation (TransAgg) aggregates evidence around changes in their relative motion, such as grasp and release. GeoDelta describes surface motion independently of embodiment-specific controls, providing a shared human--robot quasi-action for action-free pretraining on EgoManip-5000H followed by robot mid-training on Robot-30H. Gnomon4D leads on RoboTwin~2.0, VLABench, and RoboDojo, and outperforms the strongest real-robot baseline by 7.0 and 16.0 percentage points under in-distribution and out-of-distribution settings, respectively.