In this work, we tackle the problem of learning universal robotic dexterous grasping from a
point cloud observation under a table-top setting. The goal is to grasp and lift up objects in …

How human interact with objects depends on the functional roles of the target objects, which
introduces the problem of affordance-aware hand-object interaction. It requires a large …

The proliferation of Large Language Models (LLMs) has s fueled a shift in robot learning
from automation towards general embodied Artificial Intelligence (AI). Adopting foundation …

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 …

Vision-based human-to-robot handover is an important and challenging task in human-robot
interaction. Recent work has attempted to train robot policies by interacting with dynamic …

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 …

In this work we propose a novel discriminative framework for dexterous grasp generation
named Dexterous Grasp TRansformer (DGTR) capable of predicting a diverse set of feasible …

While there have been significant strides in dexterous manipulation, most of it is limited to
benchmark tasks like in-hand reorientation which are of limited utility in the real world. The …

Humans excel at transferring manipulation skills across diverse object shapes, poses, and
appearances due to their understanding of semantic correspondences between different …

Multi-finger grasping relies on high quality training data, which is hard to obtain: human data
is hard to transfer and synthetic data relies on simplifying assumptions that reduce grasp …