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
大学図書館所蔵 全2件
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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