Recent Advances in Robot Learning: Machine Learning - the Springer International Series in Engineering and Computer Science Reprinted from Machine Learning, 23:2-3, 1996 edition

Marc Notes: Special issue of Maching learning, v.23, nos. 2 & 3, May/June 1996Table of Contents: Machine Learning.- Real-World Robotics: Learning, to Plan for Robust Execution.- Robot Programming by Demonstration (RPD): Supporting the Induction by Human Interaction.- Performance Improvement of Robot Continuous-Path Operation through Iterative Learning Using Neural Networks.- Learning Controllers for Industrial Robots.- Active Learning for Vision-Based Robot Grasping.- Purposive Behavior Acquisition for a Real Robot by Vision-Based Reinforcement Learning.- Learning Concepts from Sensor Data of a Mobile Robot. Publisher Marketing: This work contains seven papers on robot learning written by leading researchers in the field. As the selection of papers illustrates, the field of robot learning is both active and diverse. A variety of machine learning methods, ranging from inductive logic programming to reinforcement learning, is being applied to many subproblems in robot perception and control, often with objectives as diverse as parameter calibration and concept formulation.

Contributor Bio:  Thrun, Sebastian Sebastian Thrun is Associate Professor in the Computer Science Department at Stanford University and Director of the Stanford AI Lab.