Quantifying Teaching Behavior in Robot Learning from Demonstration

Aran Sena*, Matthew Howard

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)
308 Downloads (Pure)

Abstract

Learning from demonstration allows for rapid deployment of robot manipulators to a great many tasks, by relying on a person showing the robot what to do rather than programming it. While this approach provides many opportunities, measuring, evaluating, and improving the person’s teaching ability has remained largely unexplored in robot manipulation research. To this end, a model for learning from demonstration is presented here that incorporates the teacher’s understanding of, and influence on, the learner. The proposed model is used to clarify the teacher’s objectives during learning from demonstration, providing new views on how teaching failures and efficiency can be defined. The benefit of this approach is shown in two experiments (n=30 and n=36, respectively), which highlight the difficulty teachers have in providing effective demonstrations, and show how ̴169-180% –180% improvement in teaching efficiency can be achieved through evaluation and feedback shaped by the proposed framework, relative to unguided teaching.

Original languageEnglish
Pages (from-to)54-72
Number of pages19
JournalINTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
Volume39
Issue number1
Early online date14 Nov 2019
DOIs
Publication statusPublished - Jan 2020

Keywords

  • learning from demonstration
  • robotics
  • machine learning
  • machine teaching
  • human robot interaction
  • manipulation
  • Trajectory planning
  • human computer interaction
  • framework
  • imitation learning
  • learning from examples

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