Published 1989
| Version Published
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Learning in Tele-autonomous Systems using Soar
Abstract
Robo-Soar is a high-level robot arm control system implemented in Soar. Robo-Soar learns to perform simple block manipulation tasks using advice from a human. Following learning, the system is able to perform similar tasks without external guidance. Robo-Soar corrects its knowledge by accepting advice about relevance of features in its domain, using a unique integration of analytic and empirical learning techniques.
Additional Information
This research was sponsored by grant NCC2-517 from NASA Ames and ONR grant N00014-88-K-0554. We would like to thank Karen McMahon for implementing Soar 5.0, and Mark Wiesmeyer for developing and implementing the Soar input and output interfaces. Without these extension to Soar, Robo-Soar would not have been possible. We also like to thank the staff of the Robotics Laboratory for help with the tele-autonomous software and hardware. Finally, we thank Paul Rosenbloom and Allen Newell for ideas relating to this work.Attached Files
Published - Laird_ProceedingsOfTheNasaConferenceOnSpace_1989.pdf
Files
Laird_ProceedingsOfTheNasaConferenceOnSpace_1989.pdf
Additional details
Identifiers
- Eprint ID
- 36210
- Resolver ID
- CaltechAUTHORS:20130107-150307467
Funding
- NASA
- NCC2-517
- Office of Naval Research (ONR)
- N00014-88-K-0554
Dates
- Created
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2013-01-07Created from EPrint's datestamp field
- Updated
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2019-10-03Created from EPrint's last_modified field