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Automation and Intelligent Control for Robotics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1653

Special Issue Editors

Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: intelligent unmanned systems; intelligent control; sliding mode control and model predictive control
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Guest Editor
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: nonlinear systems; intelligent control; complex dynamic systems; filtering and fault diagnosis

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Guest Editor
School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China
Interests: tethered spacecraft control; sliding mode control and constrained control
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Guest Editor
School of Aeronautics and Astronautics, Sichuan University, Chengdu 610065, China
Interests: multi-agent systems; intelligent control; model predictive control; security control; fault detection

Special Issue Information

Dear Colleagues,

In recent years, robotics has achieved significant developments driven by advancements in automation engineering, artificial intelligence, and sensor technologies. These developments have enhanced robots' capabilities and expanded their applications across various fields including industry, agriculture, manufacturing, healthcare, and space exploration. In the fields of automation and intelligent control for robotics, improving control accuracy and system stability, sensor data integration and data processing, and task decision and planning abilities have always been important research issues.

This Special Issue aims to put together original research and review articles on recent advances, technologies, solutions, applications, and challenges in the field of automation and intelligent control for robotics. Application areas of interest include (but are not limited to) automation and intelligent control for industrial robots, space robots, underwater robots, medical robots, servicing robots, educational robots, and so on.

Potential topics include, but are not limited to, the following:

  • Autonomous navigation and path planning for robotics;
  • Intelligent control algorithms for robotics;
  • Intelligent robots and systems;
  • Micro electro mechanical systems;
  • Mobile robots and intelligent autonomous systems;
  • Precision motion control;
  • Optomechatronics integration;
  • Robot calibration and automation;
  • Sensors and detection technology;
  • Robot design, development, and advanced control;
  • Advanced decision and robust control.

Dr. Xiaolei Li
Prof. Dr. Yi Zeng
Dr. Ganghui Shen
Dr. Zhaoke Ning
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. Applied Sciences 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

  • intelligent control
  • automation control
  • precision motion control
  • advanced process control
  • sensor technology
  • fault diagnosis
  • planning and control
  • robust control

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Published Papers (1 paper)

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Research

23 pages, 2436 KiB  
Article
Expert-Trajectory-Based Features for Apprenticeship Learning via Inverse Reinforcement Learning for Robotic Manipulation
by Francisco J. Naranjo-Campos, Juan G. Victores and Carlos Balaguer
Appl. Sci. 2024, 14(23), 11131; https://doi.org/10.3390/app142311131 - 29 Nov 2024
Viewed by 1248
Abstract
This paper explores the application of Inverse Reinforcement Learning (IRL) in robotics, focusing on inferring reward functions from expert demonstrations of robot arm manipulation tasks. By leveraging IRL, we aim to develop efficient and adaptable techniques for learning robust solutions to complex tasks [...] Read more.
This paper explores the application of Inverse Reinforcement Learning (IRL) in robotics, focusing on inferring reward functions from expert demonstrations of robot arm manipulation tasks. By leveraging IRL, we aim to develop efficient and adaptable techniques for learning robust solutions to complex tasks in continuous state spaces. Our approach combines Apprenticeship Learning via IRL with Proximal Policy Optimization (PPO), expert-trajectory-based features, and the application of a reverse discount. The feature space is constructed by sampling expert trajectories to capture essential task characteristics, enhancing learning efficiency and generalizability by concentrating on critical states. To prevent the vanishing of feature expectations in goal states, we introduce a reverse discounting application to prioritize feature expectations in final states. We validate our methodology through experiments in a simple GridWorld environment, demonstrating that reverse discounting enhances the alignment of the agent’s features with those of the expert. Additionally, we explore how the parameters of the proposed feature definition influence performance. Further experiments on robotic manipulation tasks using the TIAGo robot compare our approach with state-of-the-art methods, confirming its effectiveness and adaptability in complex continuous state spaces across diverse manipulation tasks. Full article
(This article belongs to the Special Issue Automation and Intelligent Control for Robotics)
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