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Autonomous Robots

, Volume 40, Issue 2, pp 211–227 | Cite as

Simultaneously learning actions and goals from demonstration

  • Baris AkgunEmail author
  • Andrea Thomaz
Article

Abstract

Our research aim is to develop interactions and algorithms for learning from naïve human teachers through demonstration. We introduce a novel approach to leverage the goal-oriented nature of human teachers by learning an action model and a goal model simultaneously from the same set of demonstrations. We use robot motion data to learn an action model for executing the skill. We use a generic set of perceptual features to learn a goal model and use it to monitor the executed action model. We evaluate our approach with data from 8 naïve teachers demonstrating two skills to the robot. We show that the goal models in the perceptual feature space are consistent across users and correctly recognize demonstrations in cross-validation tests. We additionally observe that a subset of users were not able to teach a successful action model whereas all of them were able to teach a mostly successful goal model. When the learned action models are executed on the robot, the success was on average 66.25 %. Whereas the goal models were on average 90 % correct at deciding on success/failure of the executed action, which we call monitoring.

Keywords

Learning from demonstration Goal learning Human–robot interaction 

Notes

Acknowledgments

This work has been supported by US National Science Foundation CAREER award #0953181, and by US Office of Naval Research grant #N000141410120.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.College of ComputingGeorgia Institute of TechnologyAtlantaUSA

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