{"title":"Exploring unknown environments: motivated developmental learning for autonomous navigation of mobile robots","authors":"","doi":"10.1007/s11370-023-00504-3","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>How to realize flexible behavior decision making is an important prerequisite for mobile robots to perform various tasks. To solve the problems of poor real-time performance and adaptability of traditional methods, this paper proposes a method that simulates cerebellar function through developmental network, and simulates the function of “what” and “where” channels in the visual system as well as the neuromodulatory mechanisms of dopamine and serotonin, so as to improve the adaptability of cerebellar model to behavioral decision making under supervised learning strategies. At the same time, this paper pays special attention to the strategy of simulating cerebellar reinforcement learning. By simulating the sleep recall mechanism of hippocampus and the neuromodulatory mechanism of acetylcholine and norepinephrine, mobile robots can have continuous and stable learning ability in unfamiliar environment, and improve the real-time and adaptability of their behavioral decision making. Simulation results in both static and dynamic environments, as well as the results in the static physical environment, validate the potential of this model, indicating that the cerebellar model based on reinforcement learning plays an important role in the behavioral decision making of mobile robots.</p>","PeriodicalId":48813,"journal":{"name":"Intelligent Service Robotics","volume":"8 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intelligent Service Robotics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11370-023-00504-3","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0
Abstract
How to realize flexible behavior decision making is an important prerequisite for mobile robots to perform various tasks. To solve the problems of poor real-time performance and adaptability of traditional methods, this paper proposes a method that simulates cerebellar function through developmental network, and simulates the function of “what” and “where” channels in the visual system as well as the neuromodulatory mechanisms of dopamine and serotonin, so as to improve the adaptability of cerebellar model to behavioral decision making under supervised learning strategies. At the same time, this paper pays special attention to the strategy of simulating cerebellar reinforcement learning. By simulating the sleep recall mechanism of hippocampus and the neuromodulatory mechanism of acetylcholine and norepinephrine, mobile robots can have continuous and stable learning ability in unfamiliar environment, and improve the real-time and adaptability of their behavioral decision making. Simulation results in both static and dynamic environments, as well as the results in the static physical environment, validate the potential of this model, indicating that the cerebellar model based on reinforcement learning plays an important role in the behavioral decision making of mobile robots.
期刊介绍：
The journal directs special attention to the emerging significance of integrating robotics with information technology and cognitive science (such as ubiquitous and adaptive computing,information integration in a distributed environment, and cognitive modelling for human-robot interaction), which spurs innovation toward a new multi-dimensional robotic service to humans. The journal intends to capture and archive this emerging yet significant advancement in the field of intelligent service robotics. The journal will publish original papers of innovative ideas and concepts, new discoveries and improvements, as well as novel applications and business models which are related to the field of intelligent service robotics described above and are proven to be of high quality. The areas that the Journal will cover include, but are not limited to: Intelligent robots serving humans in daily life or in a hazardous environment, such as home or personal service robots, entertainment robots, education robots, medical robots, healthcare and rehabilitation robots, and rescue robots (Service Robotics); Intelligent robotic functions in the form of embedded systems for applications to, for example, intelligent space, intelligent vehicles and transportation systems, intelligent manufacturing systems, and intelligent medical facilities (Embedded Robotics); The integration of robotics with network technologies, generating such services and solutions as distributed robots, distance robotic education-aides, and virtual laboratories or museums (Networked Robotics).
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