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Learning Obstacle Avoidance Parameters from Operator Behavior

Bradley Hamner, Sanjiv Singh and Sebastian Scherer
Carnegie Mellon University, Special Issue on Machine Learning Based Robotics in Unstructured Environments, Journal of Field Robotics, Vol. 23, No. 12-Nov, pp. 1037-1058, December, 2006

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Abstract

This paper concerns an outdoor mobile robot that learns to avoid collisions by observing a human driver operate a vehicle equipped with sensors that continuously produce a map of the local environment. We have implemented steering control that models human behavior in trying to avoid obstacles while trying to follow a desired path. Here we present the formulation for this control system and its independent parameters and then show how these parameters can be automatically estimated by observing a human driver. We also present results from operation on an autonomous robot as well as in simulation, and compare the results from our method to another commonly used learning method. We find that the proposed method generalizes well and is capable of learning from a small number of samples.

BibTeX Reference
@article{Hamner-2006-9635,
title = {Learning Obstacle Avoidance Parameters from Operator Behavior},
author = {Bradley Hamner and Sanjiv Singh and Sebastian Scherer},
booktitle = {Special Issue on Machine Learning Based Robotics in Unstructured Environments, Journal of Field Robotics},
publisher = {Wiley InterScience},
grantID = {111 River St., Hoboken, NJ 07030},
school = {Robotics Institute , Carnegie Mellon University},
month = {December},
year = {2006},
volume = {23},
number = {12-Nov},
pages = {1037-1058},
address = {Pittsburgh, PA},
}
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