Articulated object manipulation is a fundamental yet challenging task in robotics. Due to
significant geometric and semantic variations across object categories, previous …

Perceiving and manipulating 3D articulated objects in diverse environments is essential for
home-assistant robots. Recent studies have shown that point-level affordance provides …

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 …

Visual affordance recognition is an important research topic in robotics, human-computer
interaction, and other computer vision tasks. In recent years, deep learning-based …

Learning to manipulate 3D objects in an interactive environment has been a challenging
problem in Reinforcement Learning (RL). In particular, it is hard to train a policy that can …

Learning a generalizable object manipulation policy is vital for an embodied agent to work in
complex real-world scenes. Parts, as the shared components in different object categories …

Understanding and manipulating deformable objects (eg, ropes and fabrics) is an essential
yet challenging task with broad applications. Difficulties come from complex states and …

Grounding 3D object affordance seeks to locate objects'" action possibilities" regions in the
3D space, which serves as a link between perception and operation for embodied agents …

This work proposes a retrieve-and-transfer framework for zero-shot robotic manipulation,
dubbed RAM, featuring generalizability across various objects, environments, and …

Keypoint-based representation has proven advantageous in various visual and robotic
tasks. However, the existing 2D and 3D methods for detecting keypoints mainly rely on …