Incremental estimation of users' expertise level

Pamela Carreno-Medrano, Abhinav Dahiya, Stephen L. Smith, Dana Kulic

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Estimating a user's expertise level based on observations of their actions will result in better human-robot collaboration, by enabling the robot to adjust its behaviour and the assistance it provides according to the skills of the particular user it's interacting with. This paper details an approach to incrementally and continually estimate the expertise of a user whose goal is to optimally complete a given task. The user's expertise level, here represented as a scalar parameter, is estimated by evaluating how far their actions are from optimal. The proposed approach was tested using data from an online study where participants were asked to complete various instances of a simulated kitting task. An optimal planner was used to estimate the 'goodness' of all available actions at any given task state. We found that our expertise level estimates correlate strongly with observed after-task performance metrics and that it is possible to differentiate novices from experts after observing, on average, 33% of the errors made by the novices.

Original languageEnglish
Title of host publication2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2019)
EditorsJohn-John Cabibihan, Mary Anne Williams, T. Asokan, Laxmidhar Behera
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781728126227
ISBN (Print)9781728126234
Publication statusPublished - 2019
Externally publishedYes
EventIEEE/RSJ International Symposium on Robot and Human Interactive Communication 2019 - New Delhi, India
Duration: 14 Oct 201918 Oct 2019
Conference number: 28th (Proceedings)


ConferenceIEEE/RSJ International Symposium on Robot and Human Interactive Communication 2019
Abbreviated titleRO-MAN 2019
CityNew Delhi
Internet address


  • expertise estimation
  • HRI
  • human profiling

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