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 …

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

Robotic manipulation can be formulated as inducing a sequence of spatial displacements:
where the space being moved can encompass an object, part of an object, or end effector. In …

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 …

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 …

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

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 …

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 …

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 …

Humans can feel, weigh and grasp diverse objects, and simultaneously infer their material
properties while applying the right amount of force—a challenging set of tasks for a modern …