Antecedents of trust in human-robot collaborations

Oleson, Kristin E.; Billings, Deborah R.; Kocsis, Vivien; Chen, Jessie Y. C.; Hancock, Peter A.

doi:10.1109/cogsima.2011.5753439

Cited by 64 publications

(43 citation statements)

References 20 publications

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“…Related work over the past decade can be observed in various application domains, i.e. urban search and rescue [2,3], military [4] and industry [5].…”

Section: Introductionmentioning

confidence: 99%

ChibiFace: A sensor-rich Android tablet-based interface for industrial robotics

Nurimbetov

Saudabayev

Temiraliuly

et al. 2015

2015 IEEE/SICE International Symposium on System Integration (SII)

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Tablet computers are advanced sensor-rich units equipped with high computational power and extended communication capability. Even though their primary usage is in consumer electronics, we believe they have high potential in human-robot interaction. Tablets can be programmed to replicate various functions of the head such as vision, hearing, speaking and facial articulations. In this work, we present tablet computer-based robot head ChibiFace. Built on top of the Android operating system, it uses native Android and third party open-source libraries to implement various audio and visual signal processing algorithms, machine learning and system integration with different robot platforms. Experiments carried out in industrial robotics settings show that ChibiFace can enhance the safety of human-robot interaction. Rapid pace of advancement and functional expansion in tablet computers driven by the intense market competition foretells that highly capable, portable and customizable tablet-based human-robot interfaces might be widely accessible in the near future.

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“…Related work over the past decade can be observed in various application domains, i.e. urban search and rescue [2,3], military [4] and industry [5].…”

Section: Introductionmentioning

confidence: 99%

ChibiFace: A sensor-rich Android tablet-based interface for industrial robotics

Nurimbetov

Saudabayev

Temiraliuly

et al. 2015

2015 IEEE/SICE International Symposium on System Integration (SII)

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“…However, for the operator to use the robot to its full potential it needs to trust the robot. A lack of trust could result in unnecessarily monitoring the robot's actions, underutilizing the robot, or not using it at all [1].…”

Section: Introductionmentioning

confidence: 99%

Improving Trust-Guided Behavior Adaptation Using Operator Feedback

Floyd

Drinkwater

Aha

2015

Case-Based Reasoning Research and Development

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It is important for robots to be trusted by their human teammates so that they are used to their full potential. This paper focuses on robots that can estimate their own trustworthiness based on their performance and adapt their behavior to engender trust. Ideally, a robot can receive feedback about its performance from teammates. However, that feedback can be sporadic or non-existent (e.g., if teammates are busy with their own duties), or come in a variety of forms (e.g., different teammates using different vocabularies). We describe a case-based algorithm that allows a robot to learn a model of feedback and use that model to adapt its behavior. We evaluate our system in a simulated robotics domain by showing that a robot can learn a model of operator feedback and use that model to improve behavior adaptation.

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“…In these situations, their human operator(s) would likely issue commands or delegate tasks to the robot to reduce their workload or more efficiently achieve team goals. A lack of trust in the robot could result in the humans under-utilizing it, unnecessarily monitoring the robot's actions, or possibly not using it at all [1].…”

Section: Introductionmentioning

confidence: 99%

How Much Do You Trust Me? Learning a Case-Based Model of Inverse Trust

Floyd

Drinkwater

Aha

2014

Case-Based Reasoning Research and Development

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Robots can be important additions to human teams if they improve team performance by providing new skills or improving existing skills. However, to get the full benefits of a robot the team must trust and use it appropriately. We present an agent algorithm that allows a robot to estimate its trustworthiness and adapt its behavior in an attempt to increase trust. It uses case-based reasoning to store previous behavior adaptations and uses this information to perform future adaptations. We compare case-based behavior adaptation to behavior adaptation that does not learn and show it significantly reduces the number of behaviors that need to be evaluated before a trustworthy behavior is found. Our evaluation is in a simulated robotics environment and involves a movement scenario and a patrolling/threat detection scenario.

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Antecedents of trust in human-robot collaborations

Cited by 64 publications

References 20 publications

ChibiFace: A sensor-rich Android tablet-based interface for industrial robotics

ChibiFace: A sensor-rich Android tablet-based interface for industrial robotics

Improving Trust-Guided Behavior Adaptation Using Operator Feedback

How Much Do You Trust Me? Learning a Case-Based Model of Inverse Trust

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