Achieving Intelligence in Mobility - Incorporating Learning Capabilities in Real-World Mobile Robots
M. Kaiser, Volker Klingspor, J.R. del Millan, M. Accame, F. Wallner, R. Dillmann · 1995
This paper presents an integrated approach to the application of Machine Learning techniques for the enhancement of mobile robots' skills. It identifies the learning tasks that can be observed throughout a number of typical applications of mobile robots and puts those tasks into perspective with respect to both existing and newly developed learning techniques. The actual realization of the approach has been carried out on the two mobile robots PRIAMOS and TESEO, which are both operating in a real office environment. In this context, several experimental results are presented.