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首页> 外文期刊>International journal of human-computer studies >Assessing physical workload for human-robot peer-based teams
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Assessing physical workload for human-robot peer-based teams

机译：评估基于人类机器人对等团队的物理工作量

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摘要

Peer-based human-robot teams involve both teammates working in the same physical space and contributing to the same goals. Predictions of human performance based upon environmental, internal, task and organizational influences have proven useful, but these predictive methods have not yet been proven to apply to human-robot peer-based team situations. Physical workload is an important component of overall workload and influences human performance. The presented research examines physical workload metrics, appraises predictive models of physical workload, and investigates the impact of human-robot peer-based teaming situations on physical workload. Two evaluations are presented. The Guided evaluation required participants to follow guided instructions provided by a partner, either a remotely located human or a locally situated robot. The Collaborative evaluation required collaboration and teaming with joint decisions with either the locally situated human or robot partner. The results from both evaluations show that overall workload and subjectively rated physical workload was lower for the human-robot teams than the human-human teams; however, the physiologically measured physical workload was higher for the human-robot teams. The lack of a collocated human partner during the Guided evaluation did not affect the workload results. The modeling techniques and empirical measures used in the evaluations can be extended to other human-robot team situations.
机译：基于对等的人类机器人团队让两个队友在相同的物理空间中工作并为相同的目标做出贡献。基于环境，内部，任务和组织的影响对人员绩效的预测已被证明是有用的，但尚未证明这些预测方法适用于基于人机对等的团队环境。物理工作量是整体工作量的重要组成部分，并且会影响人类绩效。提出的研究检查了物理工作量指标，评估了物理工作量的预测模型，并调查了基于人机对等的团队协作情况对物理工作量的影响。提出了两个评估。指导评估要求参与者遵循合作伙伴（位于远处的人或位于本地的机器人）提供的指导。协作评估需要与本地人员或机器人合作伙伴进行协作，并共同做出决策。两项评估的结果均表明，人机团队的总体工作量和主观评估的体力工作量均低于人机团队;然而，通过生理测量的身体负荷对于机器人团队来说更高。在指导性评估过程中缺少同伴的人伴不会影响工作量结果。评估中使用的建模技术和经验方法可以扩展到其他人机团队情况。

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