Abstract
In social environment navigation, robots inevitably exhibit behaviors that are perceived as inappropriate by humans. Current robots lack the ability to adapt to such human perceptions, leading to repeated inappropriate behaviors. This study employs a mixed-methods approach to explore human-preferred robot adaptations, combining qualitative data from a series of human-robot interactions and a semi-structured interview, and quantitative data from an online survey. 12 participants were recruited to interact with a mobile robot in an indoor setting, reporting 139 instances of inappropriate robot behaviors. The subsequent semi-structured interviews regarding these instances yielded 9 types of inappropriate behaviors and 10 major types of human-preferred robot adaptations, ranging from general ones, such as stopping the motion, to more specific ones, like moving away and then stopping. Additionally, 12 human-preferred adaptations were selected from the interview data and presented to the same participants through an online survey to evaluate their effectiveness in addressing the inappropriate behaviors previously identified. The results reveal the human preference for the robot to move to the side and then stop in most scenarios, which might serve as a general adaptation for addressing inappropriate robot navigation behaviors.
Original language | English |
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Pages (from-to) | 11826-11833 |
Number of pages | 8 |
Journal | IEEE Robotics and Automation Letters |
Volume | 9 |
Issue number | 12 |
DOIs | |
Publication status | Published - 2024 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- human factors and human-in-the-loop
- methods and tools for robot system design
- physical human-robot interaction
- social HRI