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UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondence
Garment manipulation (e. g., unfolding, folding and hanging clothes) is essential for future robots to accomplish home-assistant tasks, while highly challenging due to the diversity of garment configurations, geometries and deformations.
ImageManip: Image-based Robotic Manipulation with Affordance-guided Next View Selection
This framework is designed to capture multiple perspectives of the target object and infer depth information to complement its geometry.
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.
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