A General Classifier of Whisker Data Using Stationary Naive Bayes: Application to BIOTACT Robots
- Cite this paper as:
- Lepora N.F. et al. (2011) A General Classifier of Whisker Data Using Stationary Naive Bayes: Application to BIOTACT Robots. In: Groß R., Alboul L., Melhuish C., Witkowski M., Prescott T.J., Penders J. (eds) Towards Autonomous Robotic Systems. TAROS 2011. Lecture Notes in Computer Science, vol 6856. Springer, Berlin, Heidelberg
A general problem in robotics is how to best utilize sensors to classify the robot’s environment. The BIOTACT project (BIOmimetic Technology for vibrissal Active Touch) is a collaboration between biologists and engineers that has led to many distinctive robots with artificial whisker sensing capabilities. One problem is to construct classifiers that can recognize a wide range of whisker sensations rather than constructing different classifiers for specific features. In this article, we demonstrate that a stationary naive Bayes classifier can perform such a general classification by applying it to various robot experiments. This classifier could be a key component of a robot able to learn autonomously about novel environments, where classifier properties are not known in advance.
KeywordsBIOTACT Active touch Whiskers Bayes’ rule Classifier
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