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Analysis of human–robot interaction at the DARPA Robotics Challenge Finals.

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Title: Analysis of human–robot interaction at the DARPA Robotics Challenge Finals.
Authors: Norton, Adam1 anorton@cs.uml.edu, Ober, Willard2, Baraniecki, Lisa3, Mccann, Eric4, Scholtz, Jean5, Shane, David2, Skinner, Anna3, Watson, Robert2, Yanco, Holly4
Source: International Journal of Robotics Research. Jun2017, Vol. 36 Issue 5-7, p483-513. 31p.
Subjects: United States. Defense Advanced Research Projects Agency, Humanoid robots, Human-robot interaction, Virtual reality, Rescue robots
Abstract: In June 2015, the Defense Advanced Research Projects Agency (DARPA) Robotics Challenge (DRC) Finals were held in Pomona, California. The DRC Finals served as the third phase of the program designed to test the capabilities of semi-autonomous, remote humanoid robots to perform disaster response tasks with degraded communications. All competition teams were responsible for developing their own interaction method to control their robot. Of the 23 teams in the competition, 20 consented to participate in this study of human–robot interaction (HRI). The evaluation team observed the consenting teams during task execution in their control rooms (with the operators), and all 23 teams were observed on the field during the public event (with the robot). A variety of data were collected both before the competition and on-site. Each participating team’s interaction methods were distilled into a set of characteristics pertaining to the robot, operator strategies, control methods, and sensor fusion. Each task was decomposed into subtasks that were classified according to the complexity of the mobility and/or manipulation actions being performed. Performance metrics were calculated regarding the number of task attempts, performance time, and critical incidents, which were then correlated to each team’s interaction methods. The results of this analysis suggest that a combination of HRI characteristics, including balancing the capabilities of the operator with those of the robot and multiple sensor fusion instances with variable reference frames, positively impacted task performance. A set of guidelines for designing HRI with remote, semi-autonomous humanoid robots is proposed based on these results. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Robotics Research is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Analysis of human–robot interaction at the DARPA Robotics Challenge Finals.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Robotics+Research%22">International Journal of Robotics Research</searchLink>. Jun2017, Vol. 36 Issue 5-7, p483-513. 31p.
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  Data: <searchLink fieldCode="DE" term="%22United+States%2E+Defense+Advanced+Research+Projects+Agency%22">United States. Defense Advanced Research Projects Agency</searchLink><br /><searchLink fieldCode="DE" term="%22Humanoid+robots%22">Humanoid robots</searchLink><br /><searchLink fieldCode="DE" term="%22Human-robot+interaction%22">Human-robot interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Virtual+reality%22">Virtual reality</searchLink><br /><searchLink fieldCode="DE" term="%22Rescue+robots%22">Rescue robots</searchLink>
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  Data: In June 2015, the Defense Advanced Research Projects Agency (DARPA) Robotics Challenge (DRC) Finals were held in Pomona, California. The DRC Finals served as the third phase of the program designed to test the capabilities of semi-autonomous, remote humanoid robots to perform disaster response tasks with degraded communications. All competition teams were responsible for developing their own interaction method to control their robot. Of the 23 teams in the competition, 20 consented to participate in this study of human–robot interaction (HRI). The evaluation team observed the consenting teams during task execution in their control rooms (with the operators), and all 23 teams were observed on the field during the public event (with the robot). A variety of data were collected both before the competition and on-site. Each participating team’s interaction methods were distilled into a set of characteristics pertaining to the robot, operator strategies, control methods, and sensor fusion. Each task was decomposed into subtasks that were classified according to the complexity of the mobility and/or manipulation actions being performed. Performance metrics were calculated regarding the number of task attempts, performance time, and critical incidents, which were then correlated to each team’s interaction methods. The results of this analysis suggest that a combination of HRI characteristics, including balancing the capabilities of the operator with those of the robot and multiple sensor fusion instances with variable reference frames, positively impacted task performance. A set of guidelines for designing HRI with remote, semi-autonomous humanoid robots is proposed based on these results. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of International Journal of Robotics Research is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jun2017
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