by M Beetz and T Belker
Abstract:
This paper investigates the problem of improving the performance of general state-of-the-art robot control systems by autonomously adapting them to specific tasks and environments. We propose model- and test-based transformational learning (MTTL) as a computational model for performing this task. MTTL uses abstract models of control systems and environments in order to propose promising adaptations. To account for model deficiencies resulting from abstraction, hypotheses are statistically tested based on experimentation in the physical world. We describe XfrmLearn, an implementation of MTTL, and apply it to the problem of indoor navigation. We present experiments in which XfrmLearn improves the navigation performance of a state-of-the-art high-speed navigation system for a given set of navigation tasks by up to 44 percent.
Reference:
Environment and Task Adaptation for Robotic Agents (M Beetz and T Belker), In Procs. of the 14th European Conference on Artificial Intelligence (ECAI-2000) (W. Horn, ed.), 2000.
Bibtex Entry:
@inproceedings{beetz_environment_2000,
author = {M Beetz and T Belker},
title = {Environment and Task Adaptation for Robotic Agents},
booktitle = {Procs. of the 14th European Conference on Artificial Intelligence
({ECAI-2000)}},
year = {2000},
editor = {Horn, W.},
pages = {648–652},
abstract = {This paper investigates the problem of improving the performance of
general state-of-the-art robot control systems by autonomously adapting
them to specific tasks and environments. We propose model- and test-based
transformational learning ({MTTL)} as a computational model for performing
this task. {MTTL} uses abstract models of control systems and environments
in order to propose promising adaptations. To account for model deficiencies
resulting from abstraction, hypotheses are statistically tested based
on experimentation in the physical world. We describe {XfrmLearn},
an implementation of {MTTL}, and apply it to the problem of indoor
navigation. We present experiments in which {XfrmLearn} improves
the navigation performance of a state-of-the-art high-speed navigation
system for a given set of navigation tasks by up to 44 percent.},
}