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Trust in Human-Autonomy Teaming: A Review of Trust Research from the US Army Research Laboratory Robotics Collaborative Technology Alliance

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Advances in Human Factors in Robots and Unmanned Systems (AHFE 2018)

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Abstract

Trust is paramount to the development of effective human-robot teaming. It becomes even more important as robotic systems evolve to make both independent and interdependent decisions in high-risk, dynamic environments. Yet, despite decades of research looking at trust in human-interpersonal teams, human-animal teams, and human-automation interaction, there are still a number of critical research gaps related to human-robot trust. The US Army Research Laboratory Robotics Collaborative Technology Alliance (RCTA) is a 10-year program with government, industry and academia combining to conduct collaborative research across four major robotic technical areas of intelligence, perception, human-robot interaction, and manipulation and mobility. This paper describes findings from over 60 publications and 49 presentations describing research conducted as part of the RCTA from 2010 to 2017 to address these critical gaps on human-robot trust.

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Notes

  1. 1.

    More information about ARL’s past and current CTAs and CRAs are located on ARL’s website http://www.arl.army.mil/www/default.cfm?page=93.

  2. 2.

    The biannual program plan is available at http://www.arl.army.mil/www/default.cfm?page=392.

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Acknowledgments

The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.

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Correspondence to Kristin E. Schaefer .

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Schaefer, K.E., Hill, S.G., Jentsch, F.G. (2019). Trust in Human-Autonomy Teaming: A Review of Trust Research from the US Army Research Laboratory Robotics Collaborative Technology Alliance. In: Chen, J. (eds) Advances in Human Factors in Robots and Unmanned Systems. AHFE 2018. Advances in Intelligent Systems and Computing, vol 784. Springer, Cham. https://doi.org/10.1007/978-3-319-94346-6_10

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