Evaluating Fluency in Human–Robot Collaboration
By Guy Hoffman. IEEE Transactions on Human-Machine Systems, 2019
469 citations recorded by the source corpus.
Collaborative fluency is the coordinated meshing of joint activities between members of a well-synchronized team. In recent years, researchers in human–robot collaboration have been developing robots to work alongside humans aiming not only at task efficiency, but also at human–robot fluency. As part of this effort, we have developed a number of metrics to evaluate the level of fluency in human–robot shared-location teamwork. While these metrics are being used in existing research, there has been no systematic discussion on how to measure fluency and how the commonly used metrics perform and compare. In this paper, we codify subjective and objective human–robot fluency metrics, provide an analytical model for four objective metrics, and assess their dynamics in a turn-taking framework. We also report on a user study linking objective and subjective fluency metrics and survey recent use of these metrics in the literature.
Subfields: Social Psychology
View Paper: https://www.semanticscholar.org/paper/92baecd5a48721970bce78b38fa1459f5694cf1a
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Citations in the corpus
- Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration (2020, methodology): “…each trial, subjects were asked to rate the AI agent they just worked with on a scale of 1 to 7 based on three criteria commonly used in prior work (Hoffman, 2019): i) how much the agent knew about the true goal (1 - no knowledge, 4 - some knowledge, 7 - perfect knowledge), ii) how helpful you…”