Industrial robots and associated control methods are continuously developing. With the recent progress in the field of artificial intelligence, new perspectives in industrial robot …
H Oliff, Y Liu, M Kumar, M Williams, M Ryan - Journal of Manufacturing …, 2020 - Elsevier
For many contemporary manufacturing processes, autonomous robotic operators have become ubiquitous. Despite this, the number of human operators within these processes …
Y Liu, H Xu, D Liu, L Wang - Robotics and Computer-Integrated …, 2022 - Elsevier
Deep reinforcement learning (DRL) has proven to be an effective framework for solving various complex control problems. In manufacturing, industrial robots can be trained to learn …
J Hua, L Zeng, G Li, Z Ju - Sensors, 2021 - mdpi.com
Dexterous manipulation of the robot is an important part of realizing intelligence, but manipulators can only perform simple tasks such as sorting and packing in a structured …
The majority of robots in factories today are operated with conventional control strategies that require individual programming on a task-by-task basis, with no margin for error. As an …
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 …
Human–Robot Collaboration (HRC) is an interdisciplinary research area that has gained attention within the smart manufacturing context. To address changes within manufacturing …
To facilitate the personalized smart manufacturing paradigm with cognitive automation capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
V Samsonov, KB Hicham, T Meisen - Engineering Applications of Artificial …, 2022 - Elsevier
Abstract The field of Neural Combinatorial Optimization (NCO) offers multiple learning- based approaches to solve well-known combinatorial optimization tasks such as Traveling …