... for restricted objects. Design/methodology/approach A deep reinforcement learning framework is proposed to learn synergetic push-shift-grasp ( SPSG ) policies from visual observations. The action-values (Q) are modeled by Q maps outputted by three separate branches on the top of a shared backbone and a multi...

... performance. Hence, this paper aims to present next best pose calibration with deep reinforcement learning (NBP-CalibDRL), an automated hand-eye calibration method based on deep reinforcement learning (DRL), designed to enhance both calibration accuracy and stability. Design/methodology/approach NBP...

...Guanzheng Wang; Yinbo Xu; Zhihong Liu; Xin Xu; Xiangke Wang; Jiarun Yan Purpose This paper aims to realize a fully distributed multi-UAV collision detection and avoidance based on deep reinforcement learning (DRL). To deal with the problem of low sample efficiency in DRL and speed up the training...

..., using Monodepth shortens the prediction time and improves the prediction ability. Path planning FastSLAM Active SLAM Deep reinforcement learning It is vital that mobile agents have autonomy when performing tasks. In indoor environments, mobile agents need to be capable of autonomous...

... physical training samples and low efficiency, this paper proposes an offline pre-training of the attitude controller using the identification model as a priori knowledge of online training in the real physical environment. Design/methodology/approach The deep reinforcement learning (DRL) of continuous...

...Ke Xu; Fengge Wu; Junsuo Zhao Purpose Recently, deep reinforcement learning is developing rapidly and shows its power to solve difficult problems such as robotics and game of GO. Meanwhile, satellite attitude control systems are still using classical control technics such as proportional...