Making Robot Learning Controllable: A Case Study in Robot Navigation (bibtex)
by Alexandra Kirsch, Michael Schweitzer, Michael Beetz
Abstract:
In many applications the performance of learned robot controllers drags behind those of the respective hand-coded ones. In our view, this situation is caused not mainly by deficiencies of the learning algorithms but rather by an insufficient embedding of learning in robot control programs. This paper presents a case study in which RoLL, a robot control language that allows for explicit representations of learning problems, is applied to learning robot navigation tasks. The case study shows that RoLL's constructs for specifying learning problems (1) make aspects of autonomous robot learning explicit and controllable; (2) have an enormous impact on the performance of the learned controllers and therefore encourage the engineering of high performance learners; (3) make the learning processes repeatable and allow for writing bootstrapping robot controllers. Taken together the approach constitutes an important step towards engineering controllers of autonomous learning robots.
Reference:
Alexandra Kirsch, Michael Schweitzer, Michael Beetz, "Making Robot Learning Controllable: A Case Study in Robot Navigation", In Proceedings of the ICAPS Workshop on Plan Execution: A Reality Check, 2005.
Bibtex Entry:
@inproceedings{kirsch05making,
  author    = {Alexandra Kirsch and Michael Schweitzer and Michael Beetz},
  title     = {Making Robot Learning Controllable: A Case Study in Robot Navigation},
  booktitle = {Proceedings of the ICAPS Workshop on Plan Execution: A Reality Check},
  year      = {2005},
  bib2html_pubtype = {Workshop Paper},
  bib2html_rescat  = {Learning,Planning,Action},
  bib2html_groups  = {Cogito, AGILO},
  abstract = {In many applications the performance of learned robot controllers
              drags behind those of the respective hand-coded ones. In our view,
              this situation is caused not mainly by deficiencies of the learning
              algorithms but rather by an insufficient embedding of learning in
              robot control programs.
              This paper presents a case study in which RoLL, a robot control
              language that allows for explicit representations of learning
              problems, is applied to learning robot navigation tasks. The case
              study shows that RoLL's constructs for specifying learning
              problems (1) make aspects of autonomous robot learning explicit
              and controllable; (2) have an enormous impact on the
              performance of the learned controllers and therefore encourage the
              engineering of high performance learners; (3) make the learning
              processes repeatable and allow for writing bootstrapping robot
              controllers.  Taken together the approach constitutes an important
              step towards engineering controllers of autonomous learning
              robots.}
}
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