Toward Socially Aware Robots

Abstract

The growth and aging of the world population is causing an increase in the number of vulnerable individuals who require continuous support. However, health and social care systems do not possess enough human and material resources to address this growing demand. Social robots are a potential solution to help relieve this pressure by providing affordable, personalized, and continuous support to vulnerable individuals that can complement existing health and social care systems.

Nevertheless, state-of-the-art social robots struggle to sustain user engagement, especially in the long term, which ultimately limits their usability. Importantly, it is precisely through long-term interactions that social robots have the most potential to systematically tackle the overload in care systems. Therefore, enabling social robots to sustain user engagement is of utmost importance to facilitate their real-world deployment and societal impact.

The ability to sustain user engagement is strongly linked with the behaviors displayed by social robots and, consequently, with the approaches used to control said behavior. In state-of-the-art social robotics, most behavioral control approaches rely on either rule-based systems or machine-learning approaches, where both paradigms struggle to generate adaptive and personalized robot behavior. To sustain user engagement, social robots must replicate the responsiveness and adaptability that characterize human-to-human interactions, including being sensitive to others’ mental states. However, behavioral controllers of social robots do not typically model the dynamic mental states of users, nor do they adapt robot behavior to those states.

Therefore, this thesis addresses this crucial limitation by presenting a behavioral control framework for social robots that models the dynamics of user mental states and systematically generates robot behavior according to these dynamics. To do so, the proposed framework is grounded in systems and control-theoretic methods, which enable transparent and systematic decision-making.

The first step toward such a behavioral control framework is presented in Chapter 2, where a dynamic mathematical model of human perception, cognition, and decision-making is developed based on Theory of Mind. This model represents the dynamic evolution of beliefs, goals, and emotions as a state-space formulation, enabling systematic integration of the model with systems and control-theoretic approaches that can be used to steer the behavior of social robots. The model’s capability to estimate the mental states and predict the actions of users was evaluated in an experiment with human volunteers.

Building on the dynamic user model introduced in Chapter 2, Chapter 3 presents a predictive, model-based behavioral control framework for social robots. The model is incorporated into a closed-loop, constrained multi-objective optimization problem that systematically selects robot actions to steer the interaction toward desired user mental states. This chapter constitutes the first integration of systems and control-theoretic methods for behavioral control of social robots, enabling transparent, adaptive, and cognition-aware decision-making. The proposed controller was validated in an experiment with human participants interacting with a humanoid robot, demonstrating improved engagement and sustained interaction quality.

Since the performance of model-based controllers depends on accurate estimation of the user’s mental states, Chapter 4 presents three model-based state estimators for social robots that embed the dynamic model presented in Chapter 2. These estimators explicitly address the sparse and subjective nature of measurements in human-social robot interaction by personalizing process and measurement noise covariance matrices and proposing three tailored Extended Kalman Filter variants. The methods were validated using data from an experiment involving human volunteers interacting with a humanoid robot, demonstrating reduced cumulative prediction errors and improved robustness of mental state tracking over time.

To position the proposed behavioral control framework within the field of social robotics, Chapter 5 presents a systematic review of control approaches for behavior generation in social robots. The survey identifies dominant algorithmic paradigms and analyzes their underlying assumptions, strengths and limitations, and capacity to support adaptive and socially appropriate behavior in long-term human-social robot interactions. Building on this review, the chapter then synthesizes cross-category insights, evaluates control approaches by interaction length, system role, and application, and outlines emerging research directions toward more adaptive, interpretable, and cognitively grounded behavioral control frameworks. By clarifying existing strengths and gaps, this chapter motivates the need for transparent, model-based, systems-theoretic behavioral control architectures.

Together, these contributions establish a coherent, theoretically grounded behavioral control framework that bridges cognitive modeling, state estimation, and behavioral control for social robots. This thesis demonstrates that treating human-social robot interaction as a dynamic control problem, where mental states are estimated and steered over time, is both feasible and effective. The proposed framework, therefore, offers a concrete path to deploying social robots that can meaningfully support individuals across sustained, real-world interactions.