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Programming Robots using High-Level Task Descriptions

Andrew J. Martignoni III and William D. Smart.
In "Supervisory Control of Learning and Adaptive Systems: Papers from the 2004 AAAI Workshop", Mike Rosenstein and Mohammad Ghavamzadeh (eds)., pages 49-54, June 2004.

Available in AAAI Technical Report WS-04-10.

In order to be truly robust, deployed robot systems must be capable of adaptation. No matter how much we know about a particular task or environment, some amount of on-site tweaking is inevitable if we want the system to perform as well as it can. This tweaking causes the behavior of the robot to change, often by only a small amount, to better fit its environment. Machine learning can be used to "program" mobile robots, given some initial knowledge. We present a method of gathering this knowledge without the need for technical expertise in robotics or programming. The method relies on semanitic information from restricted natural language descriptions of the task to be performed. The behavior can then be modified interactively without knowledge of the underlying control mechanisms. This method allows us to tap into the experience of local experts who have done the task before, without having to train them first in computers or robotics.

Paper: [PDF]
External link: [link]

@inproceedings{aaai2004b,
  author = {Martignoni III, Andrew J. and Smart, William D.},
  editor = {Rosenstein, Mike and Ghavamzadeh, Mohammad},
  title = {Programming Robots using High-Level Task Descriptions},
  booktitle = {Supervisory Control of Learning and Adaptive Systems: Papers from the 2004 {AAAI} Workshop},
  pages = {49--54},
  note = {Available in {AAAI} Technical Report {WS-04-10}.},
  month = {June},
  year = {2004}
}