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Titlebook: Making Robots Smarter; Combining Sensing an Katharina Morik,Michael Kaiser,Volker Klingspor Book 1999 Springer Science+Business Media New Y

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发表于 2025-3-21 18:06:25 | 显示全部楼层 |阅读模式
书目名称Making Robots Smarter
副标题Combining Sensing an
编辑Katharina Morik,Michael Kaiser,Volker Klingspor
视频videohttp://file.papertrans.cn/622/621712/621712.mp4
图书封面Titlebook: Making Robots Smarter; Combining Sensing an Katharina Morik,Michael Kaiser,Volker Klingspor Book 1999 Springer Science+Business Media New Y
描述.Making Robots Smarter. is a book about learning robots. Ittreats this topic based on the idea that the integration of sensingand action is the central issue. In the first part of the book,aspects of learning in execution and control are discussed. Methodsfor the automatic synthesis of controllers, for active sensing, forlearning to enhance assembly, and for learning sensor-based navigationare presented. Since robots are not isolated but should serve us, thesecond part of the book discusses learning for human-robotinteraction. Methods of learning understandable concepts for assembly,monitoring, and navigation are described as well as optimizing theimplementation of such understandable concepts for a robot‘s real-timeperformance. .In terms of the study of embodied intelligence, .Making RobotsSmarter. asks how skills are acquired and where capabilities ofexecution and control come from. Can they be learned from examples orexperience? What is the role of communication in the learningprocedure? Whether we name it one way or the other, the methodologicalchallenge is that of integrating learning capabilities into robots.
出版日期Book 1999
关键词Navigation; communication; intelligence; learning; optimization; robot; sensing
版次1
doihttps://doi.org/10.1007/978-1-4615-5239-0
isbn_softcover978-1-4613-7388-9
isbn_ebook978-1-4615-5239-0
copyrightSpringer Science+Business Media New York 1999
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Introduction to Skill Learnings that map robot perceptions directly onto robot actions by means of a strategy (a control function or policy, resp.) that is goal-oriented. Examples of such elementary skills are force control, peg-into-hole insertion, navigation through a door,targeted parameterization of an active sensor system, et cetera.
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Learning Sensor-Based Navigation or the effect of its actions, and learns on-line using only raw sensory data collected as it moves. The experimental results show that a few trials suffice for the robot to navigate efficiently in a real environment of moderate complexity.
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Learning To Classifyation problem are outlined. Then we will present the methods and tools that were developed in the context of the work that was being done, namely the pattern recognition toolbox . and the supervised learning algorithm Q*. Finally we will apply the results of the previous theories in the context of Machine tool supervision.
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Program Optimization for Real-Time Performancehat speed up its evaluation. These are a program transformation method, a compilation method, and an efficient forward inference method. The latter is part of a performance system capable of inferring high-level concepts from the sensory input of a mobile robot on-line and in real time.
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Contact Estimation for Compliant Motion Controlcribes how this sensory information can be used in an assembly fine-motion planner and also during task execution; two approaches, one analytical and the other learning approaches, are presented and compared.
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