Robot Learning: Mapping from Perception to Action

A special issue of Robotics (ISSN 2218-6581).

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 2913

Special Issue Editor


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Guest Editor
Department of Systems Science, School of Mathematics, Southeast University, Nanjing 211189, China
Interests: distributed control theory and control engineering; autonomous intelligent system; swarm intelligence; machine learning; reinforcement learning; distributed optimization, intelligent decision making and game theory; cyber-physical systems
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Special Issue Information

Dear Colleagues,

Today, robots have become widespread in various fields, such as manufacturing, medical treatment, service, agriculture, and even entertainment applications. Due to the increasingly complex tasks, robots have evolved into part of an integrated system, instead of merely a tool. Manual and fixed programming are inadequate to cope with the challenge, which leads to growing interest in applying machine learning and statistics approached in the robotics community. Equipped with advanced learning algorithms, robots are able to better observe and imitate human behavior, autonomously master new skills, and adapt to different environments. 

This Special Issue is set to present new advances in the field of robot learning, including computer vision, natural language processing, reinforcement learning technologies, distributed learning and optimization, etc., and to explore the untapped potential of robots today.

Prof. Dr. Guanghui Wen
Guest Editor

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. Robotics is an international peer-reviewed open access monthly 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 1800 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.

Published Papers (1 paper)

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Research

17 pages, 6151 KiB  
Article
Research on Game-Playing Agents Based on Deep Reinforcement Learning
by Kai Zhao, Jia Song, Yuxie Luo and Yang Liu
Robotics 2022, 11(2), 35; https://doi.org/10.3390/robotics11020035 - 18 Mar 2022
Cited by 6 | Viewed by 2250
Abstract
Path planning is a key technology for the autonomous mobility of intelligent robots. However, there are few studies on how to carry out path planning in real time under the confrontation environment. Therefore, based on the deep deterministic policy gradient (DDPG) algorithm, this [...] Read more.
Path planning is a key technology for the autonomous mobility of intelligent robots. However, there are few studies on how to carry out path planning in real time under the confrontation environment. Therefore, based on the deep deterministic policy gradient (DDPG) algorithm, this paper designs the reward function and adopts the incremental training and reward compensation method to improve the training efficiency and obtain the penetration strategy. The Monte Carlo experiment results show that the algorithm can effectively avoid static obstacles, break through the interception, and finally reach the target area. Moreover, the algorithm is also validated in the Webots simulator. Full article
(This article belongs to the Special Issue Robot Learning: Mapping from Perception to Action)
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