Body schema learning for robotic manipulators from visual self-perception.
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.
Sturm
Jürgen
J
University of Freiburg, Department of Computer Science, Georges-Köhler-Allee 79, Freiburg, Germany. sturm@informatik.uni-freiburg.de
Plagemann
Christian
C
Burgard
Wolfram
W
D000222:Adaptation, Physiological; D000465:Algorithms; D001696:Biomechanical Phenomena; D006801:Humans; D007858:Learning / Q000502:physiology*; D008954:Models, Biological; D009068:Movement; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011597:Psychomotor Performance / Q000502:physiology*; D012371:Robotics / Q000379:methods*; D012649:Self Concept; D014796:Visual Perception / Q000502:physiology*