Using locally weighted regression for robot learning

The use of locally weighted regression in memory-based robot learning is explored. A local model is formed to answer each query, using a weighted regression in which close points (similar experiences) are weighted more than distant points (less relevant experiences). This approach implements a philosophy of modeling a complex function with many simple local models. The author explains how an appropriate distance metric or measure of similarity can be found, and how the distance metric is used. How irrelevant input variables and terms in the local model are detected is also explained. An example from the control of a robot arm is used to compare this approach with other robot control and learning techniques. >

Authors

• Christopher G. Atkeson

Keywords

• Input variables
• Cognitive robotics
• Learning
• Robot control
• Polynomials
• Interference
• Neural networks
• Feedforward neural networks
• Artificial intelligence
• Laboratories
• Training Set
• Fitting Parameters
• Global Model
• Feed-forward Network
• Constant Term
• Output Function
• Quadratic Model
• Value Dimensions
• Ridge Regression
• Regression Weights
• Cross-validation Error
• Robot Manipulator
• Nonparametric Regression
• Close Points
• Nearest Neighbor Approach
• Query Point
• Mathematical Software
• Academic Press
• Robot Learning
• Input Terms
• Local Fitting
• Model Terms
• Robotic Arm
• Motor Learning
• Least-squares
• Irrelevant Variables
• Technical Report
• Relevant Experience