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Other titles in the Springer Tracts in Advanced Robotics series:
Springer Tracts in Advanced Robotics #89: Approaches to Probabilistic Model Learning for Mobile Manipulation Robotsby Jurgen Sturm
Synopses & Reviews
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: ·
This book presents novel learning techniques that enable mobile platforms with one or more robotic manipulators to autonomously adapt to new or changing situations.
This book presents techniques that enable mobile manipulation robots to autonomously adapt to new situations. Covers kinematic modeling and learning; self-calibration; tactile sensing and object recognition; imitation learning and programming by demonstration.
Table of Contents
Introduction.- Basics.- Body Schema Learning.- Learning Kinematic Models of Articulated Objects.- Vision-based Perception of Articulated Objects.- Object Recognition using Tactile Sensors.- Object State Estimation using Tactile Sensors.- Learning Manipulation Tasks by Demonstration.- Conclusions.
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