Effects of Anthropomorphism and Accountability on Trust in Human Robot Interaction

Natarajan, Manisha; Gombolay, Matthew

doi:10.1145/3319502.3374839

Cited by 141 publications

(85 citation statements)

References 20 publications

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“…For example, Chidambaram et al (2012) found that participants complied more with a robot's suggestion when the robot used bodily cues (i.e., proximity to the participant, gaze, and gestures) dependent on its physical embodiment compared to only vocal cues. Natarajan and Gombolay (2020) have found that behavior and anthropomorphism of a robot are the most significant factors in predicting the trust and compliance with the robot. Goetz et al (2003) found that people complied more with a robot whose demeanor matched the seriousness of the task.…”

Section: Introductionmentioning

confidence: 99%

Robot Authority in Human-Robot Teaming: Effects of Human-Likeness and Physical Embodiment on Compliance

et al. 2021

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The anticipated social capabilities of robots may allow them to serve in authority roles as part of human-machine teams. To date, it is unclear if, and to what extent, human team members will comply with requests from their robotic teammates, and how such compliance compares to requests from human teammates. This research examined how the human-likeness and physical embodiment of a robot affect compliance to a robot's request to perseverate utilizing a novel task paradigm. Across a set of two studies, participants performed a visual search task while receiving ambiguous performance feedback. Compliance was evaluated when the participant requested to stop the task and the coach urged the participant to keep practicing multiple times. In the first study, the coach was either physically co-located with the participant or located remotely via a live-video. Coach type varied in human-likeness and included either a real human (confederate), a Nao robot, or a modified Roomba robot. The second study expanded on the first by including a Baxter robot as a coach and replicated the findings in a different sample population with a strict chain of command culture. Results from both studies showed that participants comply with the requests of a robot for up to 11 min. Compliance is less than to a human and embodiment and human-likeness on had weak effects on compliance.

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Section: Introductionmentioning

confidence: 99%

Robot Authority in Human-Robot Teaming: Effects of Human-Likeness and Physical Embodiment on Compliance

et al. 2021

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“…With humans, our decision to trust a person often hinges upon first impressions [30]. The same goes for robots-people often judge a robot's trustworthiness based on its physical appearance [11,[31][32][33]. For instance, robots that have human-like characteristics tend to be viewed as more trustworthy [31], but only up to a certain point.…”

Section: Start With Designmentioning

confidence: 99%

“…The same goes for robots-people often judge a robot's trustworthiness based on its physical appearance [11,[31][32][33]. For instance, robots that have human-like characteristics tend to be viewed as more trustworthy [31], but only up to a certain point. Robots that appear to be highly similar, but still quite distinguishable, from humans are seen as less trustworthy [32,33].…”

Section: Start With Designmentioning

confidence: 99%

Trust in Robots: Challenges and Opportunities

2020

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Purpose of Review To assess the state-of-the-art in research on trust in robots and to examine if recent methodological advances can aid in the development of trustworthy robots. Recent Findings While traditional work in trustworthy robotics has focused on studying the antecedents and consequences of trust in robots, recent work has gravitated towards the development of strategies for robots to actively gain, calibrate, and maintain the human user's trust. Among these works, there is emphasis on endowing robotic agents with reasoning capabilities (e.g., via probabilistic modeling). Summary The state-of-the-art in trust research provides roboticists with a large trove of tools to develop trustworthy robots. However, challenges remain when it comes to trust in real-world human-robot interaction (HRI) settings: there exist outstanding issues in trust measurement, guarantees on robot behavior (e.g., with respect to user privacy), and handling rich multidimensional data. We examine how recent advances in psychometrics, trustworthy systems, robot-ethics, and deep learning can provide resolution to each of these issues. In conclusion, we are of the opinion that these methodological advances could pave the way for the creation of truly autonomous, trustworthy social robots.

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“…We classify robot-related factors under three subcategories: (a) Performance-related factors: These factors determine the quality of an operation performed by the robot from the human operator's point of view. Of these factors we can mention reliability, faulty behavior, frequency of fault occurrence [12], timing of error [33],transparency, feedback [53,39], level of situation awareness [5], false alarms [69], and level of autonomy [28]. (b) Behavior-related factors: Advancements in robotic systems in recent years have caused people to consider them more like teammates than tools.…”

Section: Factors Affecting Trustmentioning

confidence: 99%

“…Many factors related to the the task type and location such as rationality and revocability of the tasks [53], risk and humansafety [49], workload [12], nature of the task (e.g., nurse or security robot) [59], task duration [62,69], in-group membership [19,46], physical presence of robot [3,39] and task site [28] are thoroughly investigated by researchers in HRI. [12,11,53,49,39,33] Similarity with operator [70] Physical presence of robot in task site [3,39] Level of [28,18] Gender [70,59] In-group membership [46,19] Situation awareness, feedback and Transparency [11,39,5,63] Harmony of appearance with task [50] Duration of interaction [69,62] (b) Behavior-Related Anthropomorphism [39] Task site [28] Dialogues [33] 2. Human-Related Revocability [53] Proximity [38,40,62] Personality [53] Rationality [53,49,3] Likeability and friendliness…”

Section: Factors Affecting Trustmentioning

confidence: 99%

Modeling Trust in Human-Robot Interaction: A Survey

Khavas

Ahmadzadeh

Robinette

2020

Lecture Notes in Computer Science

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As the autonomy and capabilities of robotic systems increase, they are expected to play the role of teammates rather than tools and interact with human collaborators in a more realistic manner, creating a more human-like relationship. Given the impact of trust observed in human-robot interaction (HRI), appropriate trust in robotic collaborators is one of the leading factors influencing the performance of human-robot interaction. Team performance can be diminished if people do not trust robots appropriately by disusing or misusing them based on limited experience. Therefore, trust in HRI needs to be calibrated properly, rather than maximized, to let the formation of an appropriate level of trust in human collaborators. For trust calibration in HRI, trust needs to be modeled first. There are many reviews on factors affecting trust in HRI [22], however, as there are no reviews concentrated on different trust models, in this paper, we review different techniques and methods for trust modeling in HRI. We also present a list of potential directions for further research and some challenges that need to be addressed in future work on human-robot trust modeling.

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Effects of Anthropomorphism and Accountability on Trust in Human Robot Interaction

Cited by 141 publications

References 20 publications

Robot Authority in Human-Robot Teaming: Effects of Human-Likeness and Physical Embodiment on Compliance

Robot Authority in Human-Robot Teaming: Effects of Human-Likeness and Physical Embodiment on Compliance

Trust in Robots: Challenges and Opportunities

Modeling Trust in Human-Robot Interaction: A Survey

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