Your Robot Is Watching 2: Using Emotion Features to Predict the Intent to Deceive

Surendran, Vidullan; Mokhtari, Kasra; Wagner, Alan R.

doi:10.1109/ro-man50785.2021.9515553

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…These mistakes decrease human’s trust in them and, if left unaddressed, can have disastrous effects on human-robot team performance (Baker, Phillips, Ullman, & Keebler, 2018; Sebo, Krishnamurthi, & Scassellati, 2019). This is especially problematic in scenarios where UGVs are meant to act as not only useful tools but also as active collaborators and teammates (Azevedo-Sa et al, 2021; Rossi, Dautenhahn, Koay, & Walters, 2020; Surendran, Mokhtari, & Wagner, 2021; Xu & Howard, 2020; You & Robert Jr, 2018).…”

Section: Introductionmentioning

confidence: 99%

Promises and Trust Repair in UGVs

Esterwood,

Ali,

George

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Unmanned ground vehicles (UGVs) are autonomous robots capable of performing tasks through self- navigation and decision-making. They have the potential to replace humans in dangerous driving scenarios. However, UGVs must be viewed as trustworthy to be accepted, and like any automation, they can make mistakes that decrease human trust in them. Trust repair strategies can mitigate the consequences of trust violations, but they are not always effective. To better understand their effectiveness on UGVs, we designed a between-subjects study examining promises on a UGV’s trustworthiness. Preliminary results showed that promises had a marginal impact on overall trustworthiness but were influential in repairing benevolence but not ability or integrity. These findings have implications for the design of UGV’s and trust repair theory.

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Section: Introductionmentioning

confidence: 99%

Promises and Trust Repair in UGVs

Esterwood,

Ali,

George

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

View full text Add to dashboard Cite

show abstract

“…For example, a range of fast-food chains and restaurants have added robots to their teams [7], [8], [9] and retailers have deployed shelf-scanning robots [10], [11]. Trust, however, is required for the success of these human-robot teams [12], [13], [14], [6], [15]. Trust, or accepting the vulnerability associated with relying on others, is vital for human-robot teams but is often decreased by trust violations [16], [17], [18].…”

Section: Introductionmentioning

confidence: 99%

A Literature Review of Trust Repair in HRI

Esterwood

Robert

2022

2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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Trust is vital for effective human-robot teams. Trust is unstable, however, and it changes over time, with decreases in trust occurring when robots make mistakes. In such cases, certain strategies identified in the human-human literature can be deployed to repair trust, including apologies, denials, explanations, and promises. Whether these strategies work in the human-robot domain, however, remains largely unknown. This is primarily because of the fragmented and dispersed state of the current literature on trust repair in HRI. As a result, this paper brings together studies on trust repair in HRI and presents a more cohesive view of when apologies, denials, explanations, and promises have been seen to repair trust. In doing so, this paper also highlights possible gaps and proposes future work. This contributes to the literature in several ways but primarily provides a starting point for future research and recommendations for studies seeking to determine how trust can be repaired in HRI.

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That was not what I was aiming at! Differentiating human intent and outcome in a physically dynamic throwing task

Surendran

Wagner

2022

Auton Robot

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Recognising intent in collaborative human robot tasks can improve team performance and human perception of robots. Intent can differ from the observed outcome in the presence of mistakes which are likely in physically dynamic tasks. We created a dataset of 1227 throws of a ball at a target from 10 participants and observed that 47% of throws were mistakes with 16% completely missing the target. Our research leverages facial images capturing the person’s reaction to the outcome of a throw to predict when the resulting throw is a mistake and then we determine the actual intent of the throw. The approach we propose for outcome prediction performs 38% better than the two-stream architecture used previously for this task on front-on videos. In addition, we propose a 1D-CNN model which is used in conjunction with priors learned from the frequency of mistakes to provide an end-to-end pipeline for outcome and intent recognition in this throwing task.

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Your Robot Is Watching 2: Using Emotion Features to Predict the Intent to Deceive

Cited by 4 publications

References 15 publications

Promises and Trust Repair in UGVs

Promises and Trust Repair in UGVs

A Literature Review of Trust Repair in HRI

That was not what I was aiming at! Differentiating human intent and outcome in a physically dynamic throwing task

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