Industrial robots and associated control methods are continuously developing. With the
recent progress in the field of artificial intelligence, new perspectives in industrial robot …

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 …

In this work we introduce Wonder3D a novel method for generating high-fidelity textured
meshes from single-view images with remarkable efficiency. Recent methods based on the …

Probabilistic diffusion models have achieved state-of-the-art results for image synthesis,
inpainting, and text-to-image tasks. However, they are still in the early stages of generating …

Research and application of reinforcement learning in robotics for contact-rich manipulation
tasks have exploded in recent years. Its ability to cope with unstructured environments and …

The ability to grasp and manipulate transparent objects is a major challenge for robots.
Existing depth cameras have difficulty detecting, localizing, and inferring the geometry of …

We approach the problem of high-DOF reaching-and-grasping via learning joint planning of
grasp and motion with deep reinforcement learning. To resolve the sample efficiency issue …

Expert demonstrations are a rich source of supervision for training visual robotic
manipulation policies, but imitation learning methods often require either a large number of …

General object grasping is an important yet unsolved problem in the field of robotics. Most of
the current methods either generate grasp poses with few DoF that fail to cover most of the …

Object pose detection and tracking has recently attracted increasing attention due to its wide
applications in many areas, such as autonomous driving, robotics, and augmented reality …