Approaches to probabilistic model learning for mobile manipulation robots
著者
書誌事項
Approaches to probabilistic model learning for mobile manipulation robots
（Springer tracts in advanced robotics, 89）
Springer, c2013
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内容説明・目次
内容説明
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.
目次
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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