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Human-Robot Team Coordination with Dynamic and Latent Human Task Proficiencies Scheduling with Learning Curves

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Document pages: 11 pages

Abstract: As robots become ubiquitous in the workforce, it is essential thathuman-robot collaboration be both intuitive and adaptive. A robot s qualityimproves based on its ability to explicitly reason about the time-varying (i.e.learning curves) and stochastic capabilities of its human counterparts, andadjust the joint workload to improve efficiency while factoring humanpreferences. We introduce a novel resource coordination algorithm that enablesrobots to explore the relative strengths and learning abilities of their humanteammates, by constructing schedules that are robust to stochastic andtime-varying human task performance. We first validate our algorithmic approachusing data we collected from a user study (n = 20), showing we can quicklygenerate and evaluate a robust schedule while discovering the latest individualworker proficiency. Second, we conduct a between-subjects experiment (n = 90)to validate the efficacy of our coordinating algorithm. Results from thehuman-subjects experiment indicate that scheduling strategies favoringexploration tend to be beneficial for human-robot collaboration as it improvesteam fluency (p = 0.0438), while also maximizing team efficiency (p < 0.001).

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