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 …

This paper presents a comprehensive survey on vision-based robotic gras**. We
conclude three key tasks during vision-based robotic gras**, which are object localization …

Gras** unseen objects in unconstrained, cluttered environments is an essential skill for
autonomous robotic manipulation. Despite recent progress in full 6-DoF grasp learning …

Object gras** is critical for many applications, which is also a challenging computer vision
problem. However, for cluttered scene, current researches suffer from the problems of …

As the basis for prehensile manipulation, it is vital to enable robots to grasp as robustly as
humans. Our innate gras** system is prompt, accurate, flexible, and continuous across …

Generating grasp poses is a crucial component for any robot object manipulation task. In this
work, we formulate the problem of grasp generation as sampling a set of grasps using a …

Multi-objective optimization problems are ubiquitous in robotics, eg, the optimization of a
robot manipulation task requires a joint consideration of grasp pose configurations …

General robot gras** in clutter requires the ability to synthesize grasps that work for
previously unseen objects and that are also robust to physical interactions, such as …

We propose a new task to benchmark scene understanding of embodied agents: Situated
Question Answering in 3D Scenes (SQA3D). Given a scene context (eg, 3D scan), SQA3D …

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 …