In unstructured environments, robotic grasping tasks are frequently required to interactively
search for and retrieve specific objects from a cluttered workspace under the condition that …

Meta-Imitation Learning is a promising technique for the robot to learn a new task from observing
one or a few human demonstrations. However, it usually requires a significant number …

Currently, using human prostate cancer (PCa) tissue samples to conduct proteomics
research has generated a large amount of data; however, only a very small amount has been …

“Picking out the impurities” is a typical scenario in production line which is both time consuming
and laborious. In this article, we propose a target-oriented robotic push-grasping system …

Deep reinforcement learning (DRL) has been increasingly applied to multi-agent domains
in recent years. However, the agent explores the environment randomly, resulting in low …

In order to fulfil the requirements in high‐frequency and high‐power applications, this paper
presents a novel frequency multiplication method, in which N single‐phase voltage‐type …

This paper studies the problem of adaptive fault estimation and accommodation for a class
of stochastic nonlinear systems with unknown time-varying faults. Different from existing fault …

The inactive state has become the main status of user equipments (UEs) in 5G networks due
to its low power consumption and energy saving features. To deal with the massive paging …

The rapidly increasing amounts of mobile traffic have brought new challenges on the limited
spectrum resource. LTE on the unlicensed band, namely, LTE-U, under the carrier …

Interferometric synthetic aperture radar (InSAR) has proven to be a unique tool for investigating
large-scale surface deformation. Although the applications of InSAR are widespread, …