Integration of Machine Learning and Optimization for Robot Learning

• Published in: Recent Global Research and Education: Technological Challenges Vol. 519; pp. 349 - 355
• Main Authors: Mosavi, Amir, Varkonyi-Koczy, Annamaria R.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2016; Springer International Publishing
• Series: Advances in Intelligent Systems and Computing
• Subjects: Artificial intelligence; Cybernetics & systems theory; Machine learning; Optimization; Robotics
• ISBN: 9783319464893; 3319464892
• ISSN: 2194-5357; 2194-5365
• DOI: 10.1007/978-3-319-46490-9_47

Learning ability in Robotics is acknowledged as one of the major challenges facing artificial intelligence. Although in the numerous areas within Robotics machine learning (ML) has long identified as a core technology, recently Robot learning, in particular, has been witnessing major challenges due to the theoretical advancement at the boundary between optimization and ML. In fact the integration of ML and optimization reported to be able to dramatically increase the decision-making quality and learning ability in decision systems. Here the novel integration of ML and optimization which can be applied to the complex and dynamic contexts of Robot learning is described. Furthermore with the aid of an educational Robotics kit the proposed methodology is evaluated.