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Sensory Prediction Learning - How to Model the Self and the Environment
Ryo Saegusa - Italian Institute of Technology
For a complex autonomous robotic system such as a humanoid robot, the learning-based state prediction is considered effective to develop the body and environment model autonomously. In this paper we investigate a model of changes detection directly included in the evaluation process of the learning algorithm. The model is characterized by a function called confidence, which returns a high value if the robot’s actual state data match the predicted state data. The robot then creates the confidence map for each sensor based on the prediction error.
Keywords: Papers 2008, Industry, Research & Development (R&D) ...
Erstellt: 20.06.2012 17:57
12th IMEKO TC1 & TC7 Annecy
12th IMEKO TC1 & TC7 Joint Symposium on "Man Science & Measurement" September 03.-05.2008 Annecy, France
Erstellt: 03.09.2008 14:30