Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in ...
The new Robotics: Science and Systems conference spans all areas of robotics, bringing together researchers working on the algorithmic and mathematical foundations of robotics, robotics applications, and analysis of robotics systems. This volume contains papers presented at the inaugural conference, held at MIT in June, 2005.
The annual National Information Processing (NIPS) meeting is an important conference on neural computation. The conference draws a diverse group of attendees - physicists, neuroscientists, mathematicians, statisticians and computer scientists - and the presentations of interdisciplinary, with contributions in algorithms, learning theory, cognitive ...
The International Symposium of Robotics Research (ISRR) continues to be the premiere meeting of the International Foundation of Robotics Research (IFRR). The 12th International Symposium of Robotics Research took place October 12-15, 2005, in San Francisco, near Fisherman's Wharf, and was organized by the three editors of this book. This volume ...
This monograph describes a new family of algorithms for the simultaneous localization and mapping problem in robotics (SLAM). SLAM addresses the problem of acquiring an environment map with a roving robot, while simultaneously localizing the robot relative to this map. This problem has received enormous attention in the robotics community in the ...
Over the past three decades or so, research on machine learning and data mining has led to a wide variety of algorithms that learn general functions from experience. As machine learning is maturing, it has begun to make the successful transition from academic research to various practical applications. Generic techniques such as decision trees and ...
Papers presented at the 2003 Neural Information Processing Conference by leading physicists, neuroscientists, mathematicians, statisticians, and computer scientists.
Lifelong learning addresses situations in which a learner faces a series of different learning tasks providing the opportunity for synergy among them. Explanation-based neural network learning (EBNN) is a machine learning algorithm that transfers knowledge across multiple learning tasks. When faced with a new learning task, EBNN exploits domain ...
Since its inception in 1996, FSR, the biannual "International Conference on Field and Service Robotics" has published archival volumes of high reference value. This unique collection is the post-conference proceedings of the 4th FSR in Lake Yamanaka, Japan at July 2003. This book edited by Shin'ichi Yuta, Hajime Asama, Sebastian Thrun, Erwin ...
This work contains seven papers on robot learning written by leading researchers in the field. As the selection of papers illustrates, the field of robot learning is both active and diverse. A variety of machine learning methods, ranging from inductive logic programming to reinforcement learning, is being applied to many subproblems in robot ...
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