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A4A: Cross-Embodiment Transfer of Action-Oriented 4D Affordances from Human Demonstrations

arXiv · AI, language, vision and robotics · article · Sep 5, 2026 · UTC

Human demonstrations contain rich manipulation knowledge, but it remains unclear what information can be transferred effectively to robot control. Existing affordance representations are typically formulated as 2D masks, 3D regions, contact points, or actionability scores, and therefore primarily identify where interaction may occur. However, effective manipulation also requires modeling how interaction-relevant geometry evolves during task execution. To bridge this gap, we introduce action-oriented 4D affordances, which represent the language-conditioned future trajectories of interaction-rel

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