Teacher-Aware Active Robot Learning

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

8 Citations (Scopus)
237 Downloads (Pure)


This paper investigates Active Robot Learning strategies that take into account the effort of the user in an interactive learning scenario. Most research claims that Active Learning's sample efficiency can reduce training time and therefore the effort of the human teacher. We argue that the performance driven query selection of standard Active Learning can make the job of the human teacher difficult, resulting in a decrease in training quality due to slowdowns or increased error rates. We investigate this issue by proposing a learning strategy that aims to minimize the user's workload by taking into account the flow of the questions. We compare this strategy against a standard Active Learning strategy based on uncertainty sampling and a third strategy being an hybrid of the two. After studying in simulation the validity and the behavior of these approaches, we conducted a user study where 26 subjects interacted with a NAO robot embodying the presented strategies. We reports results from both the robot's performance and the human teacher's perspectives, observing how the hybrid strategy represents a good compromise between learning performance and user's experienced workload. Based on the results, we provide recommendations on the development of Active Robot Learning strategies going beyond robot's performance.
Original languageEnglish
Title of host publicationHRI 2019 - 14th ACM/IEEE International Conference on Human-Robot Interaction
Number of pages9
ISBN (Electronic)978-1-5386-8555-6
Publication statusPublished - 22 Mar 2019
MoE publication typeA4 Article in a conference publication
EventACM/IEEE International Conference on Human-Robot Interaction - Daegu, Korea, Republic of
Duration: 11 Mar 201914 Mar 2019
Conference number: 14

Publication series

NameACM/IEEE International Conference on Human-Robot Interaction
ISSN (Print)2167-2121
ISSN (Electronic)2167-2148


ConferenceACM/IEEE International Conference on Human-Robot Interaction
Abbreviated titleHRI
Country/TerritoryKorea, Republic of
Internet address


  • Active Learning
  • Human-robot interaction
  • Interactive machine learning


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