Machine Learning and Robot Perception [electronic resource] / edited by Bruno Apolloni, Ashish Ghosh, Ferda Alpaslan, Lakhmi Jain, Srikanta Patnaik.

Learning Visual Landmarks for Mobile Robot Topological Navigation -- Foveated Vision Sensor and Image Processing – A Review -- On-line Model Learning for Mobile Manipulations -- Continuous Reinforcement Learning Algorithm for Skills Learning in an Autonomous Mobile Robot -- Efficient Incorporation of Optical Flow into Visual Motion Estimation in Tracking -- 3-D Modeling of Real-World Objects Using Range and Intensity Images -- Perception for Human Motion Understanding -- Cognitive User Modeling Computed by a Proposed Dialogue Strategy Based on an Inductive Game Theory.

This book presents some of the most recent research results in the area of machine learning and robot perception. The chapters represent new ways of solving real-world problems. The book covers topics such as intelligent object detection, foveated vision systems, online learning paradigms, reinforcement learning for a mobile robot, object tracking and motion estimation, 3D model construction, computer vision system and user modelling using dialogue strategies. This book will appeal to researchers, senior undergraduate/postgraduate students, application engineers and scientists.