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 …
[HTML][HTML] Robot learning towards smart robotic manufacturing: A review
Robotic equipment has been playing a central role since the proposal of smart
manufacturing. Since the beginning of the first integration of industrial robots into production …
manufacturing. Since the beginning of the first integration of industrial robots into production …
A closed-loop bin picking system for entangled wire harnesses using bimanual and dynamic manipulation
This paper addresses the challenge of industrial bin picking using entangled wire
harnesses. Wire harnesses are essential in manufacturing but pose challenges in …
harnesses. Wire harnesses are essential in manufacturing but pose challenges in …
[HTML][HTML] Robotic disassembly of electric vehicle batteries: Technologies and opportunities
The demand for electric vehicle (EV) battery services, such as repair, remanufacturing, and
recycling, is rising as more EVs enter the market. Disassembly is an essential step in these …
recycling, is rising as more EVs enter the market. Disassembly is an essential step in these …
Transfer learning for machine learning-based detection and separation of entanglements in bin-picking applications
M Moosmann, F Spenrath, J Rosport… - 2022 IEEE/RSJ …, 2022 - ieeexplore.ieee.org
In this paper, we present a Domain Randomization and a Domain Adaptation approach to
transfer experience for entanglement detection and separation from simulation into a real …
transfer experience for entanglement detection and separation from simulation into a real …
Learning to dexterously pick or separate tangled-prone objects for industrial bin picking
Industrial bin picking for tangled-prone objects requires the robot to either pick up untangled
objects or perform separation manipulation when the bin contains no isolated objects. The …
objects or perform separation manipulation when the bin contains no isolated objects. The …
[HTML][HTML] GAM: General affordance-based manipulation for contact-rich object disentangling tasks
Picking up an entangled object is a difficult manipulation task due to its rich contact
dynamics. Most existing solutions fail to produce grasp poses to enable reliable …
dynamics. Most existing solutions fail to produce grasp poses to enable reliable …
Performance Comparison of Supervised and Reinforcement Learning Approaches for Separating Entanglements in a Bin-Picking Application
M Moosmann, M Kaiser, J Rosport, F Spenrath… - Stuttgart Conference on …, 2022 - Springer
Abstract Machine Learning helps to separate entanglements in Bin-Picking Applications.
The goal is to create a system that finds a path to separate an entanglement, starting from a …
The goal is to create a system that finds a path to separate an entanglement, starting from a …
Advances in Autonomous Robotic Grasping: An Overview of Reinforcement Learning Approaches
BK Farkas, P Galambos, K Széll - 2024 IEEE 6th International …, 2024 - ieeexplore.ieee.org
Reinforcement Learning (RL) has become a pivotal tool in robotic manipulation, especially
for tasks requiring adaptability, such as object grasping. By allowing robots to learn optimal …
for tasks requiring adaptability, such as object grasping. By allowing robots to learn optimal …
Background, Introduction and Motivation
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