Cost-Sensitive Learning of Classification Knowledge and Its Applications in Robotics

Traditional learning-from-examples methods assume that examples are given beforehand and all features are measured for each example. However, in many robotic domains the number of features that could be measured is very large, the cost of measuring those features is significant, and thus the robot must judiciously select which features it will measure. Finding a proper tradeoff between the accuracy (e.g., number of prediction errors) and efficiency (e.g., cost of measuring features) during learning (prior to convergence) is an important part of the problem. Inspired by such robotic domains, this article considers realistic measurement costs of features in the process of incremental learning of classification knowledge. It proposes a unified framework for learning-from-examples methods that trade off accuracy for efficiency during learning, and analyzes two methods (CS-ID3 and CS-IBL) in detail. Moreover, this article illustrates the application of such a cost-sensitive-learning method to a real robot designed for an approach-recognize task. The resulting robot learns to approach, recognize, and grasp objects on a floor effectively and efficiently. Experimental results show that highly accurate classification procedures can be learned without sacrificing efficiency in the case of both synthetic and real domains.