Where2explore: Few-shot affordance learning for unseen novel categories of articulated objects
Articulated object manipulation is a fundamental yet challenging task in robotics. Due to
significant geometric and semantic variations across object categories, previous …
significant geometric and semantic variations across object categories, previous …
Learning environment-aware affordance for 3d articulated object manipulation under occlusions
Perceiving and manipulating 3D articulated objects in diverse environments is essential for
home-assistant robots. Recent studies have shown that point-level affordance provides …
home-assistant robots. Recent studies have shown that point-level affordance provides …
Robo-abc: Affordance generalization beyond categories via semantic correspondence for robot manipulation
Enabling robotic manipulation that generalizes to out-of-distribution scenes is a crucial step
toward the open-world embodied intelligence. For human beings, this ability is rooted in the …
toward the open-world embodied intelligence. For human beings, this ability is rooted in the …
Affordancellm: Grounding affordance from vision language models
Affordance grounding refers to the task of finding the area of an object with which one can
interact. It is a fundamental but challenging task as a successful solution requires the …
interact. It is a fundamental but challenging task as a successful solution requires the …
Articulated object manipulation with coarse-to-fine affordance for mitigating the effect of point cloud noise
3D articulated objects are inherently challenging for manipulation due to the varied
geometries and intricate functionalities associated with articulated objects. Point-level …
geometries and intricate functionalities associated with articulated objects. Point-level …
LEMON: Learning 3D Human-Object Interaction Relation from 2D Images
Learning 3D human-object interaction relation is pivotal to embodied AI and interaction
modeling. Most existing methods approach the goal by learning to predict isolated …
modeling. Most existing methods approach the goal by learning to predict isolated …