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ABSTRACT

Effective learning from demonstration techniques enable complex robot behaviors to be taught from a small number of demonstrations. A number of recent works have explored interactive approaches to demonstration, in which both the robot and the teacher are able to select training examples. In this paper, we focus on a demonstration selection algorithm used by the robot to identify informative states for demonstration. Existing automated approaches for demonstration selection typically rely on a single threshold value, which is applied to a measure of action confidence. We highlight the limitations of using a single fixed threshold for a specific subset of algorithms, and contribute a method for automatically setting multiple confidence thresholds designed to target domain states with the greatest uncertainty. We present a comparison of our multi-threshold selection method to confidence-based selection using a single fixed threshold, and to manual data selection by a human teacher. Our results indicate that the automated multi-threshold approach significantly reduces the number of demonstrations required to learn the task.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.9 Robotics

General Terms:
Algorithms, Human Factors, Performance

Keywords:
human-robot interaction, learning from demonstration