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    Nhan đề: Approaches to probabilistic model learning for mobile manipulation robots /
ISBN 9783642371592
DDC 629.892
Tác giả CN Sturm, Jürgen
Nhan đề Approaches to probabilistic model learning for mobile manipulation robots / Jürgen Sturm
Thông tin xuất bản Berlin ; New York : Springer, 2013
Mô tả vật lý 204 p. ; 24 cm.
Tùng thư Springer tracts in advanced robotics, 89
Tóm tắt Mobile manipulation robots are envisioned to provide many useful services both in domestic environments as well as in the industrial context. Examples include domestic service robots that implement large parts of the housework, and versatile industrial assistants that provide automation, transportation, inspection, and monitoring services. The challenge in these applications is that the robots have to function under changing, real-world conditions, be able to deal with considerable amounts of noise and uncertainty, and operate without the supervision of an expert. This book presents novel learning techniques that enable mobile manipulation robots, i.e., mobile platforms with one or more robotic manipulators, to autonomously adapt to new or changing situations. The approaches presented in this book cover the following topics: (1) learning the robot's kinematic structure and properties using actuation and visual feedback, (2) learning about articulated objects in the environment in which the robot is operating, (3) using tactile feedback to augment the visual perception, and (4) learning novel manipulation tasks from human demonstrations. This book is an ideal resource for postgraduates and researchers working in robotics, computer vision, and artificial intelligence who want to get an overview on one of the following subjects: · kinematic modeling and learning, · self-calibration and life-long adaptation, · tactile sensing and tactile object recognition, and ·imitation learning and programming by demonstration.
Thuật ngữ chủ đề Machine learning
Thuật ngữ chủ đề Autonomous robots
Thuật ngữ chủ đề Mobile robots
Khoa Khoa Cơ khí - Điện - Điện tử - Ô tô
Địa chỉ 300Q12_Kho Mượn_02(1): 072862
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100 |aSturm, Jürgen
245 |aApproaches to probabilistic model learning for mobile manipulation robots / |cJürgen Sturm
260 |aBerlin ; New York : |bSpringer, |c2013
300 |a204 p. ; |c24 cm.
490 |aSpringer tracts in advanced robotics, 89
520 |aMobile manipulation robots are envisioned to provide many useful services both in domestic environments as well as in the industrial context. Examples include domestic service robots that implement large parts of the housework, and versatile industrial assistants that provide automation, transportation, inspection, and monitoring services. The challenge in these applications is that the robots have to function under changing, real-world conditions, be able to deal with considerable amounts of noise and uncertainty, and operate without the supervision of an expert. This book presents novel learning techniques that enable mobile manipulation robots, i.e., mobile platforms with one or more robotic manipulators, to autonomously adapt to new or changing situations. The approaches presented in this book cover the following topics: (1) learning the robot's kinematic structure and properties using actuation and visual feedback, (2) learning about articulated objects in the environment in which the robot is operating, (3) using tactile feedback to augment the visual perception, and (4) learning novel manipulation tasks from human demonstrations. This book is an ideal resource for postgraduates and researchers working in robotics, computer vision, and artificial intelligence who want to get an overview on one of the following subjects: · kinematic modeling and learning, · self-calibration and life-long adaptation, · tactile sensing and tactile object recognition, and ·imitation learning and programming by demonstration.
541 |aDự án VN Book Drive
650 |aMachine learning
650 |aAutonomous robots
650 |aMobile robots
690 |aKhoa Cơ khí - Điện - Điện tử - Ô tô
852|a300|bQ12_Kho Mượn_02|j(1): 072862
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