Towards a Theory of Longitudinal Trust Calibration in Human–Robot Teams

Visser, Ewart J. de; Peeters, Marieke; Jung, Malte; Kohn, Spencer; Shaw, Tyler H.; Pak, Richard; Neerincx, Mark A.

doi:10.1007/s12369-019-00596-x

Cited by 219 publications

(169 citation statements)

References 120 publications

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“…For example, an in-home robot can be used to improve the coordination of patient communication with care providers and to assist the patient with medication management. In order for the human-robot team to interact effectively, the human should establish appropriate trust toward the robotic agents [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Predicting Trust Dynamics in Human–Robot Teaming: A Bayesian Inference Approach

Guo

Yang

2020

Int J of Soc Robotics

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Trust in automation, or more recently trust in autonomy, has received extensive research attention in the past three decades. The majority of prior literature adopted a “snapshot” view of trust and typically evaluated trust through questionnaires administered at the end of an experiment. This “snapshot” view, however, does not acknowledge that trust is a dynamic variable that can strengthen or decay over time. To fill the research gap, the present study aims to model trust dynamics when a human interacts with a robotic agent over time. The underlying premise of the study is that by interacting with a robotic agent and observing its performance over time, a rational human agent will update his/her trust in the robotic agent accordingly. Based on this premise, we develop a personalized trust prediction model and learn its parameters using Bayesian inference. Our proposed model adheres to three properties of trust dynamics characterizing human agents’ trust development process de facto and thus guarantees high model explicability and generalizability. We tested the proposed method using an existing dataset involving 39 human participants interacting with four drones in a simulated surveillance mission. The proposed method obtained a root mean square error of 0.072, significantly outperforming existing prediction methods. Moreover, we identified three distinct types of trust dynamics, the Bayesian decision maker, the oscillator, and the disbeliever, respectively. This prediction model can be used for the design of individualized and adaptive technologies.

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Section: Introductionmentioning

confidence: 99%

“…With few exceptions (e.g. [14][15][16][17][18][19][20][21]), we have little understanding of a human agent's trust formation and evolution process after repeated interactions with a robotic agent [7,20]. Second, trust in automation is usually measured by questionnaires administered to the human agents.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Predicting Trust Dynamics in Human–Robot Teaming: A Bayesian Inference Approach

Guo

Yang

2020

Int J of Soc Robotics

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“…According to this model, trust can be divided into three components, namely dispositional, situational, and learned trust. Whereas dispositional and most situational factors can hardly be influenced, initial and dynamic learned trust result from an employee's mental model of the robot, that is, an internal representation of the cobot's characteristics from which actual expectations are derived and which is continuously modified by experiences [104,105].…”

Section: Trust In the Cobotmentioning

confidence: 99%

Success factors for introducing industrial human-robot interaction in practice: an empirically driven framework

Kopp

Baumgartner

Kinkel

2020

Int J Adv Manuf Technol

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Human-robot interaction (HRI) promises to be a means whereby manufacturing companies will be able to address current challenges like a higher demand for customization. However, despite comparably low costs, there are only few applications in practice. To date, it remains unclear which factors facilitate or hinder the successful introduction of industrial collaborative robots (cobots). In a three-step approach, we first developed a comprehensive two-dimensional framework covering three separate phases and four essential components for human-robot working systems. Secondly, we reviewed related literature to identify relevant success factors. Thirdly, in an online survey we asked leading representatives of German manufacturing companies (n = 81) to assess the importance of these factors from a practical point of view. The results reveal that besides technology-related factors like occupational safety and appropriate cobot configuration, employee-centered factors like the fear of job loss and ensuring an appropriate level of trust in the robot are considered important. However, company representatives seem to underestimate the impact of subtle measures to increase employee acceptance which could be incorporated into internal communication strategies prior to and during the introduction of cobots. Comparative analysis based on three distinct application scenarios suggests that most success factors’ practical importance is independent of the motivation for implementing HRI. Furthermore, answers from practitioners in free-text fields reveal that success factors which intuitively come to their mind such as financial factors are not necessarily perceived most important. Finally, we argue for more application-oriented research that focuses on practically relevant factors to guide HRI research, inform cobot development, and support companies in overcoming apparent barriers.

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“…In the case of self-driving vehicles, the ability to indirectly measure trust would open several design possibilities, especially for adaptive ADSs capable of conforming to drivers' trust levels and modifying their own behaviors accordingly. Trust estimations could be used in solutions for issues related to trust miscalibration-i.e., when drivers' trust in the ADS is not aligned with system's actual capabilities or reliability levels [11,24,31]. In a simplified approach, trust can be inferred with only the identification and processing of observable variables that may be measured and processed to indicate trust levels.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Estimation of Drivers’ Trust in Automated Driving Systems

Azevedo-Sa

Jayaraman

Esterwood

et al. 2020

Int J of Soc Robotics

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Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of techniques for measuring drivers’ trust in the automated driving system during real-time applications execution. One possible approach for measuring trust is through modeling its dynamics and subsequently applying classical state estimation methods. This paper proposes a framework for modeling the dynamics of drivers’ trust in automated driving systems and also for estimating these varying trust levels. The estimation method integrates sensed behaviors (from the driver) through a Kalman filter-based approach. The sensed behaviors include eye-tracking signals, the usage time of the system, and drivers’ performance on a non-driving-related task. We conducted a study ($$n=80$$ n = 80 ) with a simulated SAE level 3 automated driving system, and analyzed the factors that impacted drivers’ trust in the system. Data from the user study were also used for the identification of the trust model parameters. Results show that the proposed approach was successful in computing trust estimates over successive interactions between the driver and the automated driving system. These results encourage the use of strategies for modeling and estimating trust in automated driving systems. Such trust measurement technique paves a path for the design of trust-aware automated driving systems capable of changing their behaviors to control drivers’ trust levels to mitigate both undertrust and overtrust.

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Towards a Theory of Longitudinal Trust Calibration in Human–Robot Teams

Cited by 219 publications

References 120 publications

Modeling and Predicting Trust Dynamics in Human–Robot Teaming: A Bayesian Inference Approach

Modeling and Predicting Trust Dynamics in Human–Robot Teaming: A Bayesian Inference Approach

Success factors for introducing industrial human-robot interaction in practice: an empirically driven framework

Real-Time Estimation of Drivers’ Trust in Automated Driving Systems

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