4D-WAM: 軌跡フィールドによる世界行動モデルへの時空間認識の注入
4D-WAM: Infusing Spatiotemporal Awareness into World Action Models through Trajectory Fields
ロボットの行動生成と動画予測を統合する世界行動モデルに、3次元軌跡フィールドの時空間知識を表現整合で注入する訓練戦略を提案。局所的な動き整合と長期的な目的地整合の2つの目的関数により、軌跡レベルの時空間表現を学習し、空間理解や実行精度、汎化性を向上させる。
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著者: Lishan Yang, Wenxuan Song, Xi Wang, Pingyue Sheng, Zheng Fang, Ziyang Zhou, Junjie He, Haodong Yan, Jiayi Chen, Nan Sun, Qiao Sun, Pengwei Wang, Lingqiao Liu, Yan Wang, Yuxiang Gao, Feras Dayoub, Haoang Li
分類: cs.RO
原文アブストラクト
Building on recent advances in world models, World Action Models (WAMs) jointly model video prediction and action generation. However, they typically represent videos in 2D pixel space, creating a representation gap with 3D space in which robotic actions are executed. Recent 3D approaches introduce 3D information, but fail to fully exploit the dynamics of 3D structures. In this work, we propose 4D-WAM, a model-agnostic training strategy that injects spatiotemporal knowledge from 3D trajectory fields into WAMs through representation alignment. To this end, we introduce two complementary objectives: 1) motion alignment, which aligns temporal feature variations across adjacent frames and encourages the model to build local 4D awareness during training, and 2) destination alignment, which guides the model to infer the final destination from the source frame by minimizing the gap between their attention-like similarity distributions. Together, these objectives provide both local motion supervision and long-horizon goal guidance, enabling WAMs to learn trajectory-level spatiotemporal representations. Extensive in-distribution and out-of-distribution experiments across different base models demonstrate the model's improvements in spatial understanding, execution precision, robustness, generalization, and versatility.