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Pubmed for Handhelds

PUBMED FOR HANDHELDS

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  • Title: Body schema learning for robotic manipulators from visual self-perception.
    Author: Sturm J, Plagemann C, Burgard W.
    Journal: J Physiol Paris; 2009; 103(3-5):220-31. PubMed ID: 19665561.
    Abstract:
    We present an approach to learning the kinematic model of a robotic manipulator arm from scratch using self-observation via a single monocular camera. We introduce a flexible model based on Bayesian networks that allows a robot to simultaneously identify its kinematic structure and to learn the geometrical relationships between its body parts as a function of the joint angles. Further, we show how the robot can monitor the prediction quality of its internal kinematic model and how to adapt it when its body changes-for example due to failure, repair, or material fatigue. In experiments carried out both on real and simulated robotic manipulators, we verified the validity of our approach for real-world problems such as end-effector pose prediction and end-effector pose control.
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