From the daily movement of primate troops to the mesmerizing murmurations of starling flocks in the sky, the dynamics of animal groups on the move fascinate us with the mystery of their underlying social interactions. In this talk, I will first showcase how we combine empirical data and computational models based on self-organization to understand

Embodiment is critical for intelligence; however, the traditional concept of embodiment as movement in 3-dimensional space covers only a small slice of the way biology exploits embodiment. In this talk, I will present a framework for understanding intelligence more broadly, and show how the multiscale competency of bodies navigates many different kinds of spaces. I

More details to follow. This talk is part of David Mezey's course “Introduction to Modeling Collective Behavior, ” a seminar on collective behavior research, combined with multiple interactive elements.     Image created with DALL-E by Maria Ott.

More details to follow. This talk is part of David Mezey's course “Introduction to Modeling Collective Behavior, ” a seminar on collective behavior research, combined with multiple interactive elements.       Image created with DALL-E by Maria Ott.

More details to follow. This talk is part of David Mezey's course “Introduction to Modeling Collective Behavior, ” a seminar on collective behavior research, combined with multiple interactive elements.       Image created with DALL-E by Maria Ott.

Abstract Understanding the computational principles of embodied intelligence is central to advancing robotic systems that perceive and act in complex environments. This talk explores key principles—low power consumption, robustness, and generalizability—as they emerge in the context of motion perception and action. For visual navigation, evidence is presented that challenges the conventional SLAM paradigm, which relies