Grasping 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 grasping. We
conclude three key tasks during vision-based robotic grasping, which are object localization …

We present Neural Descriptor Fields (NDFs), an object representation that encodes both
points and relative poses between an object and a target (such as a robot gripper or a rack …

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 …

We present a framework for specifying tasks involving spatial relations between objects
using only 5-10 demonstrations and then executing such tasks given point cloud …

To enable general-purpose robots, we will require the robot to operate daily articulated
objects as humans do. Current robot manipulation has heavily relied on using a parallel …

In this letter, we explore generalizable, perception-to-action robotic manipulation for precise,
contact-rich tasks. In particular, we contribute a framework for closed-loop robotic …

We introduce KeypointDeformer, a novel unsupervised method for shape control through
automatically discovered 3D keypoints. We cast this as the problem of aligning a source 3D …

Scene representation has been a crucial design choice in robotic manipulation systems. An
ideal representation should be 3D, dynamic, and semantic to meet the demands of diverse …

Learning sensorimotor control policies from high-dimensional images crucially relies on the
quality of the underlying visual representations. Prior works show that structured latent …