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.The development of flexible mobile manipulation robots is widely envisioned as a large breakthrough in technology and is expected to ... These solutions are custom-tailored to specific applications which makes them difficult to adapt: typically, changes in the application require the manual adaptation of the robota#39;s control code, a new layout of its work cell, and possibly the reconfiguration of its hardware.
|Title||:||Approaches to Probabilistic Model Learning for Mobile Manipulation Robots|
|Publisher||:||Springer - 2013-12-12|