Tracking human interactions with a commercially-available robot over multiple days

Hortensius, Ruud; Chaudhury, Bishakha; Hoffmann, Martin; Cross, Emily S.

doi:10.12688/openreseurope.14824.1

Cited by 4 publications

(5 citation statements)

References 33 publications

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“…The robot works with an application on a tablet or smartphone device. We programmed Cozmo's behaviour for our purposes using Python (version: 3.7; [45]) and the Cozmo SDK (App version: 3.4.4).…”

Section: Methodsmentioning

confidence: 99%

“…A smaller Yes/No board (9x20cm; Figure 2C) was built to accept or reject offers. The game mechanics (i.e., the distribution of coins after each round) were programmed using Python (version: 3.7; [45]) and were displayed on a computer screen (Figure 2F). As previous work [15,16] found reciprocity to be dependent on the starting player and the opponent's game strategy, we kept the starting agent consistent (opponent child or Cozmo robot would always start) and chose a tit-for-tat game strategy for the opponent.…”

Section: Methodsmentioning

confidence: 99%

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Children’s Reciprocity and Relationship Formation with a Robot Across Age

Leisten¹,

Heyselaar²,

Bosse³

et al. 2022

Preprint

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Reciprocity, responding to another one’s actions with similar actions, is central to the formation and maintenance of relationships. Reciprocity and relationship formation change with children’s development and are key aspects in human-robot interaction. So far, it is unclear how children reciprocate and build a relationship with a social robot and how reciprocity to social robots develops with age. In the current preregistered study, we collected data from 147 children aged 5 to 12 years to investigate the developmental trajectory of reciprocity towards a social robot and the formation of a relationship with this robot. To test reciprocity, children completed an Alternated Repeated Ultimatum Game with a social entertainment robot and another child. A recently validated survey on relationship formation was used that assesses trust, closeness, and social support. Results from a linear-mixed effects Bayesian analysis indicated that children reciprocated similarly to a robot as to another child. While reciprocity differed across age with lower values for 8-10-year-olds compared to younger and older children, this difference in the developmental trajectory of reciprocity was also observed when children interacted with the robot. Exploratory analysis showed differing results for positive (reciprocating positive actions with positive actions) and negative reciprocity (reciprocating negative actions with negative actions). Children’s relationship formation with a social robot changed with age but showed different developmental trajectories for trust (linear), closeness (negative quadratic), and social support (constant). No association was found between reciprocity towards the robot and relationship formation. Our findings suggest that established theories from human-human literature, such as the developmental trajectory of reciprocity, are also relevant for human-robot interaction. Children’s age is an important determinant for how children interact with and perceive robots. This therefore needs to be considered when designing robotic systems and experiments in the future as it could influence the success and effectiveness of both.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Children’s Reciprocity and Relationship Formation with a Robot Across Age

Leisten¹,

Heyselaar²,

Bosse³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The Cozmo robot works with an application on a tablet or smartphone and interacts with the user through free play or programmed games; it has face recognition and a distinct character with emotional reactions. It has a large number (>700) of emotional and behavioral animations that are regulated and triggered through the built-in emotion engine (Hortensius et al, 2022). We programmed Cozmo's behavior using Python (version: 3.7;Python, 2022) and the Cozmo software development kit (App version: 3.4.4).…”

Section: Cozmo Robotmentioning

confidence: 99%

Supplemental Material for Children’s reciprocity and relationship formation with a robot across age.

Leisten,

Heyselaar,

Bosse

et al. 2024

Technology, Mind, and Behavior

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Reciprocity is central to the formation and maintenance of relationships. Reciprocity and relationship formation change with children's development and are key aspects in human-robot interaction. So far, it is unclear how children reciprocate and build a relationship with a social robot and how this reciprocity develops with age. In the present study, we collected data from 147 children aged 5-12 years to investigate age differences in reciprocity and relationship formation toward a social robot. To test reciprocity, children completed an Alternated Repeated Ultimatum Game with a social robot and another child. Children also completed a survey on relationship formation to assess robot-related closeness, trust, and social support. Results from a linear-mixed effects Bayesian analysis indicated that children reciprocated similarly to a robot as to another child. While reciprocity differed across age with lower values for 8-10-year-olds compared to younger and older children, these age differences in reciprocity were also observed when children interacted with the robot. Children's relationship formation with a social robot also changed with age. Our findings suggest that established theories from human-human literature (e.g., age differences in reciprocity) are also relevant for human-robot interaction. Children's age is an important determinant for how children interact with and perceive robots.

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“…Longitudinal studies that address similar questions with disembodied agents such as virtual assistants and chatbots [e.g., [52][53][54] benefit from access to users' personal devices, whereas research with physically embodied artificial agents (i.e., social robots) remains far rarer due to challenges with logistical and cost barriers to situating these devices in users' domestic settings (i.e., in their home environment) to explore single or repeated interactions. While several attempts have been made before to reduce the barriers to feasibility for such work [c.f., 33,34,38,55,56], we still know relatively little about user perceptions of and behaviours toward social robots when these take place in familiar home environments. Further insights into the challenges and opportunities afforded by placing social robots into familiar domestic settings should aid humanrobot communication in general, as well as further refine the development and utility of these machines for commercial use.…”

Section: Related Workmentioning

confidence: 99%

Building Long-Term Human–Robot Relationships: Examining Disclosure, Perception and Well-Being Across Time

Laban,

Kappas,

Morrison

et al. 2023

Int J of Soc Robotics

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While interactions with social robots are novel and exciting for many people, one concern is the extent to which people’s behavioural and emotional engagement might be sustained across time, since during initial interactions with a robot, its novelty is especially salient. This challenge is particularly noteworthy when considering interactions designed to support people’s well-being, with limited evidence (or empirical exploration) of social robots’ capacity to support people’s emotional health over time. Accordingly, our aim here was to examine how long-term repeated interactions with a social robot affect people’s self-disclosure behaviour toward the robot, their perceptions of the robot, and how such sustained interactions influence factors related to well-being. We conducted a mediated long-term online experiment with participants conversing with the social robot Pepper 10 times over 5 weeks. We found that people self-disclose increasingly more to a social robot over time, and report the robot to be more social and competent over time. Participants’ moods also improved after talking to the robot, and across sessions, they found the robot’s responses increasingly comforting as well as reported feeling less lonely. Finally, our results emphasize that when the discussion frame was supposedly more emotional (in this case, framing questions in the context of the COVID-19 pandemic), participants reported feeling lonelier and more stressed. These results set the stage for situating social robots as conversational partners and provide crucial evidence for their potential inclusion in interventions supporting people’s emotional health through encouraging self-disclosure.

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Tracking human interactions with a commercially-available robot over multiple days

Cited by 4 publications

References 33 publications

Children’s Reciprocity and Relationship Formation with a Robot Across Age

Children’s Reciprocity and Relationship Formation with a Robot Across Age

Supplemental Material for Children’s reciprocity and relationship formation with a robot across age.

Building Long-Term Human–Robot Relationships: Examining Disclosure, Perception and Well-Being Across Time

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