XEWorld：行動条件付きワールドモデルは未知のロボット形態に汎化できるか？
XEWorld: Can Action-Conditioned World Models Generalize to Unseen Robot Embodiments?
ロボット操作のための行動条件付きワールドモデルが、未見のロボット形態に対して物理ダイナミクスを捉えているかを検証するため、クロスエンボディメントテストベッドXEWorldを導入し、既存モデルの限界を分析した。
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: Yixiang Chen, Jiabing Yang, Yuan Xu, Qisen Ma, Keji He, Peiyan Li, Kai Wang, Ziheng He, Xiangnan Wu, Jing Liu, Nianfeng Liu, Yan Huang, Liang Wang
分類: cs.RO, cs.CV
原文アブストラクト
Action-conditioned world models are promising learned simulators for robotic manipulation, yet evaluating them exclusively on training robots fails to reveal whether they capture physical dynamics or merely memorize visual patterns. To answer whether a model can faithfully render a robot it has never seen, we introduce XEWorld, a controlled cross-embodiment testbed for world models that isolates embodiments by evaluating held-out robots within physically identical scenes. Our systematic analysis uncovers a shared architectural bottleneck: current models act primarily as 2D visual pattern matchers whose generalization is governed by visual similarity rather than physical kinematic similarity. Driven by this limitation, they struggle to translate abstract numeric joint actions into coherent visual trajectories, and fail to predict dynamic visual changes from static initial observations. Consequently, successfully rendering an unseen embodiment zero-shot strictly requires heavily grounded cues, specifically pixel-space actions and explicit spatial-temporal alignment. Even when bypassing this zero-shot barrier via few-shot adaptation, the forced appearance recovery triggers catastrophic forgetting of seen embodiments. Together, these failures expose a critical inability to apply learned physical dynamics to novel visual appearances, highlighting that achieving true cross-embodiment generalization requires architectural innovations that decouple visual appearance from underlying physical dynamics.
