Internal State Estimation in Groups via Active Information Gathering (2505.10415v1)

Abstract: Accurately estimating human internal states, such as personality traits or behavioral patterns, is critical for enhancing the effectiveness of human-robot interaction, particularly in group settings. These insights are key in applications ranging from social navigation to autism diagnosis. However, prior methods are limited by scalability and passive observation, making real-time estimation in complex, multi-human settings difficult. In this work, we propose a practical method for active human personality estimation in groups, with a focus on applications related to Autism Spectrum Disorder (ASD). Our method combines a personality-conditioned behavior model, based on the Eysenck 3-Factor theory, with an active robot information gathering policy that triggers human behaviors through a receding-horizon planner. The robot's belief about human personality is then updated via Bayesian inference. We demonstrate the effectiveness of our approach through simulations, user studies with typical adults, and preliminary experiments involving participants with ASD. Our results show that our method can scale to tens of humans and reduce personality prediction error by 29.2% and uncertainty by 79.9% in simulation. User studies with typical adults confirm the method's ability to generalize across complex personality distributions. Additionally, we explore its application in autism-related scenarios, demonstrating that the method can identify the difference between neurotypical and autistic behavior, highlighting its potential for diagnosing ASD. The results suggest that our framework could serve as a foundation for future ASD-specific interventions.

Internal State Estimation in Groups via Active Information Gathering

The paper "Internal State Estimation in Groups via Active Information Gathering" explores a novel framework designed to enhance the efficacy of human-robot interactions, particularly in estimating human internal states such as personality traits in group settings. This framework addresses a critical gap in prior research, which largely relies on passive observation methods, limiting scalability and real-time estimation capabilities.

Methodology and Framework

The authors propose a method for active human personality estimation by combining a personality-conditioned behavior model with an active robot information-gathering policy. The behavior model draws upon the Eysenck 3-Factor theory, encompassing Psychoticism, Extraversion, and Neuroticism, to simulate human behaviors based on personality traits. This simulation is integrated into a receding-horizon planner, which actively triggers specific human behaviors, thereby enhancing the robot's understanding and estimation of the human's personality.

The framework employs Bayesian inference to update beliefs about human personalities, adjusting based on observed behaviors. Through this active and iterative process, the robot can better discern and estimate the internal states of individuals within a group, thereby reducing estimation errors and uncertainties.

Key Findings

Through simulations and user studies involving typical adults and participants with Autism Spectrum Disorder (ASD), the approach demonstrated significant improvements in personality prediction accuracy:

• In simulations, the method was scalable to groups of tens of humans, reducing personality prediction error by 29.2% and uncertainty by 79.9% compared to passive observation methods.
• In real-world user studies, the framework successfully generalized across complex personality distributions, highlighting its robustness and applicability to diverse human personalities.
• When applied to autism-related scenarios, the method effectively differentiated between neurotypical and autistic behaviors, showcasing potential for ASD diagnosis and intervention.

Implications and Future Directions

Practically, the implications of this research are substantial for fields such as social navigation, driver assistance systems, and, notably, autism diagnosis and treatment. By leveraging active information gathering, robots can provide more personalized and contextually relevant interactions in multi-human environments. Theoretically, this work extends the capabilities of Partially Observable Markov Decision Processes (POMDP) frameworks by integrating active learning methods, setting a precedent for future studies to explore multifaceted internal state estimations.

Future research may expand upon this work by incorporating multi-modal interaction strategies, refining the human behavior model with real-world data from diverse populations, and exploring cooperative strategies involving multiple robots for even more efficient group-level internal state estimation.

In conclusion, the presented framework represents a significant advancement in the field of human-robot interaction, offering a scalable and effective solution for real-time internal state estimation in complex, multi-human settings. The proposed methods not only enhance the capabilities of robotic systems in real-world applications but also lay the groundwork for further exploration in AI-driven behavioral understanding and interaction design.