Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …

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 …

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

Object grasping 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 grasping system is prompt, accurate, flexible, and continuous across …

Abstract Purpose of Review This review provides a comprehensive overview of machine
learning approaches for vision-based robotic grasping and manipulation. Current trends and …

In this paper, we study the problem of learning vision-based dynamic manipulation skills
using a scalable reinforcement learning approach. We study this problem in the context of …

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 …

Universal picking (UP), or reliable robot grasping of a diverse range of novel objects from
heaps, is a grand challenge for e-commerce order fulfillment, manufacturing, inspection, and …

Automated building processes that enable efficient in situ resource utilization can facilitate
construction in remote locations while simultaneously offering a carbon-reducing alternative …