Objective This work examines two human–autonomy team (HAT) training approaches that target communication and trust calibration to improve team effectiveness under degraded conditions. Background Human–autonomy teaming presents challenges to teamwork, some of which may be addressed through training. Factors vital to HAT performance include communication and calibrated trust. Method Thirty teams of three, including one confederate acting as an autonomous agent, received either entrainment-based coordination training, trust calibration training, or control training before executing a series of missions operating a simulated remotely piloted aircraft. Automation and autonomy failures simulating degraded conditions were injected during missions, and measures of team communication, trust, and task efficiency were collected. Results Teams receiving coordination training had higher communication anticipation ratios, took photos of targets faster, and overcame more autonomy failures. Although autonomy failures were introduced in all conditions, teams receiving the calibration training reported that their overall trust in the agent was more robust over time. However, they did not perform better than the control condition. Conclusions Training based on entrainment of communications, wherein introduction of timely information exchange through one team member has lasting effects throughout the team, was positively associated with improvements in HAT communications and performance under degraded conditions. Training that emphasized the shortcomings of the autonomous agent appeared to calibrate expectations and maintain trust. Applications Team training that includes an autonomous agent that models effective information exchange may positively impact team communication and coordination. Training that emphasizes the limitations of an autonomous agent may help calibrate trust.
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Fluent Coordination in Proximate Human Robot Teaming
Fluent coordination is important in order for teams to work well together. In proximate teaming scenarios, fluent teams tend to perform more successfully. Recent work suggests robots can support fluency in human-robot teams a number of ways, including using nonverbal cues and anticipating human intention. However, this area of research is still in its early stages. We identify some of the key challenges in this research space, specifically individual variations during teaming, knowledge and task transfer, co-training prior to task execution, and long-term interactions. We then discuss possible paths forward, including leveraging human adaptability, to promote more fluent teaming.
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- Award ID(s):
- 1734482
- NSF-PAR ID:
- 10145650
- Date Published:
- Journal Name:
- In Proceedings of the Robotics, Science, and Systems (RSS) Workshop on AI and Its Alternatives for Shared Autonomy in Assistive and Collaborative Robotics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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