Abstract
This article describes how the kinematic models of a manipulation robot can be learned, calibrated, monitored and adapted automatically using the perception and actuation capabilities provided by the robot’s middleware. The presented technology requires only minimal human intervention by building on the concepts of self-observation and non-parametric learning. Specifically, the approach is to learn the kinematic model of a robotic manipulator 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. This article includes experiments carried out both on real and simulated robotic manipulators designed to verify the validity of the approach for real-world problems, such as end-effector pose prediction and end-effector pose control.
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Sturm, J., Plagemann, C., Burgard, W. (2012). Body Schema Learning. In: Prassler, E., et al. Towards Service Robots for Everyday Environments. Springer Tracts in Advanced Robotics, vol 76. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25116-0_12
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DOI: https://doi.org/10.1007/978-3-642-25116-0_12
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