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Accurately estimating human internal states, such as personality traits orbehavioral patterns, is critical for enhancing the effectiveness of human-robotinteraction, particularly in group settings. These insights are key inapplications ranging from social navigation to autism diagnosis. However, priormethods are limited by scalability and passive observation, making real-timeestimation in complex, multi-human settings difficult. In this work, we proposea practical method for active human personality estimation in groups, with afocus on applications related to Autism Spectrum Disorder (ASD). Our methodcombines a personality-conditioned behavior model, based on the Eysenck3-Factor theory, with an active robot information gathering policy thattriggers human behaviors through a receding-horizon planner. The robot's beliefabout human personality is then updated via Bayesian inference. We demonstratethe effectiveness of our approach through simulations, user studies withtypical adults, and preliminary experiments involving participants with ASD.Our results show that our method can scale to tens of humans and reducepersonality prediction error by 29.2% and uncertainty by 79.9% in simulation.User studies with typical adults confirm the method's ability to generalizeacross complex personality distributions. Additionally, we explore itsapplication in autism-related scenarios, demonstrating that the method canidentify the difference between neurotypical and autistic behavior,highlighting its potential for diagnosing ASD. The results suggest that ourframework could serve as a foundation for future ASD-specific interventions.

Internal State Estimation in Groups via Active Information Gathering