The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

In part one of this two-part series, we described our Gesture-Based Programming paradigm for programming by human demonstration. This paradigm depends on a pre-existing knowledge base of capabilities, collectively called "encapsulated expertise," that comprise the real-time sensorimotor primitives from which the run-time executable is constructed as well as providing the basis for interpreting the teacher's actions during programming. In this paper we present a technique based on principal components analysis, augmentable with model-based information, for learning and recognizing sensorimotor primitives. We describes simple applications of the technique to a mobile robot and a PUMA manipulator. The mobile robot learned to escape from jams while the manipulator learned guarded moves and accommodations that are composable to allow flat plate mating operations. While these initial applications are simple, they demonstrate the ability to extract primitives from demonstration, recognize the learned primitives in subsequent demonstrations, and combine and transform primitives to create different capabilities.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Text Reference

Richard Voyles, James Morrow, and Pradeep Khosla, "Gesture-Based Programming, Part 2: Primordial Learning," Intelligent Engineering through Artificial Neural Networks-Volume 6: Fuzzy Logic and Evolutionary Programming, Proceedings of ANNIE '96: Conference on Artificial Neural Networks in Engineering, November, 1996.

BibTeX Reference

@inproceedings{Voyles_1996_1712,
   author = "Richard Voyles and James Morrow and Pradeep Khosla",
   title = "Gesture-Based Programming, Part 2: Primordial Learning",
   booktitle = "Intelligent Engineering through Artificial Neural Networks-Volume 6: Fuzzy Logic and Evolutionary Programming, Proceedings of ANNIE '96: Conference on Artificial Neural Networks in Engineering",
   month = "November",
   year = "1996",
}

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