Gras** is the process of picking up an object by applying forces and torques at a set of
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …

Dexterous manipulation, especially dexterous gras**, is a primitive and crucial ability of
robots that allows the implementation of performing human-like behaviors. Deploying the …

We propose a sim-to-real framework for dexterous manipulation which can generalize to
new objects of the same category in the real world. The key of our framework is to train the …

Category-level 6D object pose estimation aims to estimate the rotation translation and size
of unseen instances within specific categories. In this area dense correspondence-based …

Dexterous multi-fingered robotic hands have a formidable action space, yet their
morphological similarity to the human hand holds immense potential to accelerate robot …

It is difficult to precisely annotate object instances and their semantics in 3D space, and as
such, synthetic data are extensively used for these tasks, eg, category-level 6D object pose …

Constructing a 3D scene capable of accommodating open-ended language queries, is a
pivotal pursuit in the domain of robotics, which facilitates robots in executing object …

Rotation estimation of high precision from an RGB-D object observation is a huge challenge
in 6D object pose estimation, due to the difficulty of learning in the non-linear space of SO …

6-DoF robotic gras** is a long-lasting but un-solved problem. Recent methods utilize
strong 3D networks to extract geometric gras** representations from depth sensors …