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Electronics
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Advanced Machine Learning for Intelligent Robotics
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Advanced Machine Learning for Intelligent Robotics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 13969

Special Issue Editors

Dr. Ji-Hyeong Han

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
Interests: intelligent robotics; human-robot interaction; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Inbae Jeong

E-Mail Website
Guest Editor
Department of Mechanical Engineering, North Dakota State University, Fargo, ND 58108, USA
Interests: robotics; artificial intelligence; motion planning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ki-baek Lee

E-Mail Website
Guest Editor
Department of Electric Engineering, College of Electronics and Information Engineering, Kwangwoon University, Seoul 01897, Korea
Interests: multi-objective optimization; evolutionary algorithm; humanoid robot; deep learning; artificial intelligence

Special Issue Information

Dear Colleagues,

Recently, machine learning represented by deep learning has developed rapidly and has been successfully applied to various fields, including computer vision, natural language processing, and decision making. Intelligent robotics is one of the promising applications of machine learning, because intelligent robots need not only robot hardware design and control but also robot vision, robot sensor fusion, robot navigation, decision making, and human-robot interaction. This may seem similar to applying machine learning to traditional fields such as computer vision and to intelligent robots, but there are notable differences because we need to consider the characteristics of robots. Therefore, this Special Issue focuses on research into applying machine learning to robotics. The scope of this Special Issue covers machine-learning-based robot control, robot navigation, robot vision, robot sensor fusion, decision making, and human-robot interaction, among others.

Prof. Dr. Ji-Hyeong Han
Prof. Dr. Inbae Jeong
Prof. Dr. Ki-baek Lee
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • robot control
  • robot navigation
  • robot vision
  • robot sensor fusion
  • robot decision making
  • human-robot interaction
  • other intelligent robotics applications based on machine learning

Published Papers (4 papers)

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Research

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15 pages, 1329 KiB  
Article
Prioritized Hindsight with Dual Buffer for Meta-Reinforcement Learning
by Sofanit Wubeshet Beyene and Ji-Hyeong Han
Electronics 2022, 11(24), 4192; https://doi.org/10.3390/electronics11244192 - 15 Dec 2022
Cited by 2 | Viewed by 1724
Abstract
Sharing prior knowledge across multiple robotic manipulation tasks is a challenging research topic. Although the state-of-the-art deep reinforcement learning (DRL) algorithms have shown immense success in single robotic tasks, it is still challenging to extend these algorithms to be applied directly to resolve [...] Read more.
Sharing prior knowledge across multiple robotic manipulation tasks is a challenging research topic. Although the state-of-the-art deep reinforcement learning (DRL) algorithms have shown immense success in single robotic tasks, it is still challenging to extend these algorithms to be applied directly to resolve multi-task manipulation problems. This is mostly due to the problems associated with efficient exploration in high-dimensional state and continuous action spaces. Furthermore, in multi-task scenarios, the problem of sparse reward and sample inefficiency of DRL algorithms is exacerbated. Therefore, we propose a method to increase the sample efficiency of the soft actor-critic (SAC) algorithm and extend it to a multi-task setting. The agent learns a prior policy from two structurally similar tasks and adapts the policy to a target task. We propose a prioritized hindsight with dual experience replay to improve the data storage and sampling technique, which, in turn, assists the agent in performing structured exploration that leads to sample efficiency. The proposed method separates the experience replay buffer into two buffers to contain real trajectories and hindsight trajectories to reduce the bias introduced by the hindsight trajectories in the buffer. Moreover, we utilize high-reward transitions from previous tasks to assist the network in easily adapting to the new task. We demonstrate the proposed method based on several manipulation tasks using a 7-DoF robotic arm in RLBench. The experimental results show that the proposed method outperforms vanilla SAC in both a single-task setting and multi-task setting. Full article
(This article belongs to the Special Issue Advanced Machine Learning for Intelligent Robotics)
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12 pages, 2556 KiB  
Article
Transformer-Based Disease Identification for Small-Scale Imbalanced Capsule Endoscopy Dataset
by Long Bai, Liangyu Wang, Tong Chen, Yuanhao Zhao and Hongliang Ren
Electronics 2022, 11(17), 2747; https://doi.org/10.3390/electronics11172747 - 31 Aug 2022
Cited by 19 | Viewed by 2829
Abstract
Vision Transformer (ViT) is emerging as a new leader in computer vision with its outstanding performance in many tasks (e.g., ImageNet-22k, JFT-300M). However, the success of ViT relies on pretraining on large datasets. It is difficult for us to use ViT to train [...] Read more.
Vision Transformer (ViT) is emerging as a new leader in computer vision with its outstanding performance in many tasks (e.g., ImageNet-22k, JFT-300M). However, the success of ViT relies on pretraining on large datasets. It is difficult for us to use ViT to train from scratch on a small-scale imbalanced capsule endoscopic image dataset. This paper adopts a Transformer neural network with a spatial pooling configuration. Transfomer’s self-attention mechanism enables it to capture long-range information effectively, and the exploration of ViT spatial structure by pooling can further improve the performance of ViT on our small-scale capsule endoscopy dataset. We trained from scratch on two publicly available datasets for capsule endoscopy disease classification, obtained 79.15% accuracy on the multi-classification task of the Kvasir-Capsule dataset, and 98.63% accuracy on the binary classification task of the Red Lesion Endoscopy dataset. Full article
(This article belongs to the Special Issue Advanced Machine Learning for Intelligent Robotics)
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Review

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24 pages, 400 KiB  
Review
Methods of Intelligent Control in Mechatronics and Robotic Engineering: A Survey
by Iuliia Zaitceva and Boris Andrievsky
Electronics 2022, 11(15), 2443; https://doi.org/10.3390/electronics11152443 - 5 Aug 2022
Cited by 13 | Viewed by 5684
Abstract
Artificial intelligence is becoming an increasingly popular tool in more and more areas of technology. New challenges in control systems design and application are related to increased productivity, control flexibility, and processing of big data. Some kinds of systems require autonomy in real-time [...] Read more.
Artificial intelligence is becoming an increasingly popular tool in more and more areas of technology. New challenges in control systems design and application are related to increased productivity, control flexibility, and processing of big data. Some kinds of systems require autonomy in real-time decision-making, while the other ones may serve as an essential factor in human-robot interaction and human influences on system performance. Naturally, the complex tasks of controlling technical systems require new modern solutions, but there remains an inextricable link between control theory and artificial intelligence. The first part of the present survey is devoted to the main intelligent control methods in technical systems. Among them, modern methods of adaptive and optimal control, fuzzy logic, and machine learning are considered. In its second part, the crucial achievements in intelligent control applications in robotic and mechatronic systems over the past decade are considered. The references are structured according to the type of such common control problems as stabilization, controller tuning, identification, parametric optimization, iterative learning, and prediction. In the conclusion, the main problems and tendencies toward intelligent control methods improvement are outlined. Full article
(This article belongs to the Special Issue Advanced Machine Learning for Intelligent Robotics)
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23 pages, 3097 KiB  
Review
From Biological Synapses to “Intelligent” Robots
by Birgitta Dresp-Langley
Electronics 2022, 11(5), 707; https://doi.org/10.3390/electronics11050707 - 25 Feb 2022
Cited by 5 | Viewed by 2583
Abstract
This selective review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis [...] Read more.
This selective review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self-organization is brought forward. Learning without prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival-relevant or task-relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for “intelligent” robotics and sensor systems. Full article
(This article belongs to the Special Issue Advanced Machine Learning for Intelligent Robotics)
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