Abstract Constraints for Safe and Robust Robot Learning from Demonstration

Authors

  • Carl L. Mueller University of Colorado Boulder

DOI:

https://doi.org/10.1609/aaai.v34i10.7136

Abstract

My thesis research incorporates high-level abstract behavioral requirements, called ‘conceptual constraints’, into the modeling processes of robot Learning from Demonstration (LfD) techniques. My most recent work introduces an LfD algorithm called Concept Constrained Learning from Demonstration. This algorithm encodes motion planning constraints as temporal Boolean operators that enforce high-level constraints over portions of the robot's motion plan during learned skill execution. This results in more easily trained, more robust, and safer learned skills. Future work will incorporate conceptual constraints into human-aware motion planning algorithms. Additionally, my research will investigate how these concept constrained algorithms and models are best incorporated into effective interfaces for end-users.

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Published

2020-04-03

How to Cite

Mueller, C. L. (2020). Abstract Constraints for Safe and Robust Robot Learning from Demonstration. Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13728-13729. https://doi.org/10.1609/aaai.v34i10.7136

Issue

Section

Doctoral Consortium Track