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Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects
Our framework explicitly estimates the geometric similarity across different categories, identifying local areas that differ from shapes in the training categories for efficient exploration while concurrently transferring affordance knowledge to similar parts of the objects.
Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation
In this paper, we study deformable object manipulation using dense visual affordance, with generalization towards diverse states, and propose a novel kind of foresightful dense affordance, which avoids local optima by estimating states' values for long-term manipulation.
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