We propose a novel, object-agnostic method for learning a universal policy for dexterous
object grasping from realistic point cloud observations and proprioceptive information under …

Robot manipulation relies on accurately predicting contact points and end-effector directions
to ensure successful operation. However learning-based robot manipulation trained on a …

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 …

Reconstructing hand-held objects from a single RGB image is an important and challenging
problem. Existing works utilizing Signed Distance Fields (SDF) reveal limitations in …

We present ArtiGrasp, a novel method to synthesize bimanual hand-object interactions that
include grasping and articulation. This task is challenging due to the diversity of the global …

The use of anthropomorphic robotic hands for assisting individuals in situations where
human hands may be unavailable or unsuitable has gained significant importance. In this …

This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM)
designed for embodied interaction, exploring a universal 3D object understanding with 3D …

Previous works concerning single-view hand-held object reconstruction typically rely on
supervision from 3D ground-truth models which are hard to collect in real world. In contrast …

The intricate kinematics of the human hand enable simultaneous grasping and manipulation
of multiple objects, essential for tasks, such as object transfer and in-hand manipulation …

Enabling robotic manipulation that generalizes to out-of-distribution scenes is a crucial step
toward open-world embodied intelligence. For human beings, this ability is rooted in the …