Recent Advances in Robot Learning: Machine Learning
Judy A. Franklin, Tom M. Mitchell, Sebastian Thrun
Springer Science & Business Media, Jun 30, 1996 - Computers - 218 pages
Recent Advances in Robot Learning 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.
While no unified robot learning framework has yet emerged to cover the variety of problems and approaches described in these papers and other publications, a clear set of shared issues underlies many robot learning problems.
These characteristics present challenges and constraints to the learning system. Since these characteristics are shared by other important real-world application domains, robotics is a highly attractive area for research on machine learning.
On the other hand, machine learning is also highly attractive to robotics. There is a great variety of open problems in robotics that defy a static, hand-coded solution.
Recent Advances in Robot Learning is an edited volume of peer-reviewed original research comprising seven invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 23, Numbers 2 and 3).
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action space active learning applied Artificial Intelligence backpropagation basic features BLOCK3 classification closed-loop control error data approximations decreasing defined described domain dynamics environment examples execution failure Figure fuzzy controller goal gradient grammar GRDT gripper hidden layer hypothesis space induction inductive logic programming industrial robots initial instantiated interactions International Conference interval knowledge L2-norm learned rules learning algorithms learning approach learning method learning steps LRFNs machine learning move multilayer perceptron neural network neuron object opening width operation output perceptual feature performance permissive planning plan parameters position postcondition preconditions predicate problem Programming by Demonstration Q-learning quality function RBFNs real robot real-world reinforcement learning representation robot control robot learning robot programming robotic system Robotics and Automation Sebastian Thrun selection sensor data sensor measurements sequence shows simulation SMART+ superquadric supervised learning techniques Thrun trajectory trials uncertainty values variables vector weights