Anqi Xu
ABSTRACT
We present OPTIMo: an Online Probabilistic Trust Inference Model for quantifying the degree of trust that a human supervisor has in an autonomous robot "worker". Represented as a Dynamic Bayesian Network, OPTIMo infers beliefs over the human's moment-to-moment latent trust states, based on the history of observed interaction experiences. A separate model instance is trained on each user's experiences, leading to an interpretable and personalized characterization of that operator's behaviors and attitudes. Using datasets collected from an interaction study with a large group of roboticists, we empirically assess OPTIMo's performance under a broad range of configurations. These evaluation results highlight OPTIMo's advances in both prediction accuracy and responsiveness over several existing trust models. This accurate and near real-time human-robot trust measure makes possible the development of autonomous robots that can adapt their behaviors dynamically, to actively seek greater trust and greater efficiency within future human-robot collaborations.
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Index Terms
- OPTIMo: Online Probabilistic Trust Inference Model for Asymmetric Human-Robot Collaborations
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