CSL: a cost-sensitive learning system for sensing and grasping objects
Ming Tan · 2002
The goal of the research reported is to build a learning robot which can survive in an unknown environment for a long time. Such a robot must learn which sensors to use, where to use them, and how to generate an inexpensive and reliable robot control procedure to accomplish its task. This is beyond machine learning methods because they usually ignore robot execution costs and are ill-prepared to handle failures. A cost-sensitive, noise-tolerant and inductive robot learning system, CSL, that represents the first steps toward achieving this goal is described, emphasizing the cost and noise issues in learning. CSL has been implemented in a real-world robot for sensing objects and selecting their grasping procedures.>