Deep reinforcement learning in smart manufacturing: A review and prospects
To facilitate the personalized smart manufacturing paradigm with cognitive automation
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
How to train your robot with deep reinforcement learning: lessons we have learned
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 …
acquiring complex behaviors from low-level sensor observations. Although a large portion of …
Deep reinforcement learning in production systems: a systematic literature review
Shortening product development cycles and fully customisable products pose major
challenges for production systems. These not only have to cope with an increased product …
challenges for production systems. These not only have to cope with an increased product …
Deep reinforcement learning for Internet of Things: A comprehensive survey
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …
communication, computing, caching and control (4Cs) problems. The recent advances in …
[HTML][HTML] A review on reinforcement learning for contact-rich robotic manipulation tasks
Í Elguea-Aguinaco, A Serrano-Muñoz… - Robotics and Computer …, 2023 - Elsevier
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 …
tasks have exploded in recent years. Its ability to cope with unstructured environments and …
[HTML][HTML] Reinforcement learning for disassembly system optimization problems: A survey
The disassembly complexity of end-of-life products increases continuously. Traditional
methods are facing difficulties in solving the decision-making and control problems of …
methods are facing difficulties in solving the decision-making and control problems of …
Tactile-rl for insertion: Generalization to objects of unknown geometry
Object insertion is a classic contact-rich manipulation task. The task remains challenging,
especially when considering general objects of unknown geometry, which significantly limits …
especially when considering general objects of unknown geometry, which significantly limits …
Coarse-to-fine imitation learning: Robot manipulation from a single demonstration
E Johns - 2021 IEEE international conference on robotics and …, 2021 - ieeexplore.ieee.org
We introduce a simple new method for visual imitation learning, which allows a novel robot
manipulation task to be learned from a single human demonstration, without requiring any …
manipulation task to be learned from a single human demonstration, without requiring any …
A review of robotic assembly strategies for the full operation procedure: planning, execution and evaluation
Y Jiang, Z Huang, B Yang, W Yang - Robotics and Computer-Integrated …, 2022 - Elsevier
The application of robots in mechanical assembly increases the efficiency of industrial
production. With the requirements of flexible manufacturing, it has become a research …
production. With the requirements of flexible manufacturing, it has become a research …
[HTML][HTML] Manipnet: neural manipulation synthesis with a hand-object spatial representation
Natural hand manipulations exhibit complex finger maneuvers adaptive to object shapes
and the tasks at hand. Learning dexterous manipulation from data in a brute force way …
and the tasks at hand. Learning dexterous manipulation from data in a brute force way …