A Mechanistic Model for Collective Motion from Sensorimotor Regularities

2026-05-19 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning, Life Sciences & Biology · Depth: Expert, extended

Summary

Researchers from Technische Universität Berlin and the Robotics Institute Germany propose a mechanistic model for collective animal behavior, moving beyond descriptive self-propelled particle models. Their model, based on the Active InterCONnect (AICON) robotics framework, simulates agents perceiving neighbors via bearing and apparent-size cues within a limited field of view, maintaining uncertain internal state estimates, and selecting actions through gradient descent on a desired social distance. This approach, which prescribes no interaction forces, reproduces diverse collective behaviors such as polarized motion, milling, ring formations, and subgroup fragmentation. A global sensitivity analysis reveals that behavioral transitions are governed by sensorimotor parameters like field of view geometry, sensory noise, turning agility (angular velocity limit $\omega_{\mathrm{max}}$), and memory, which are measurable biological quantities rather than abstract fitted constants. The model highlights how collective behavior emerges from interacting sensorimotor regularities and how species differences arise from variations in embodiment and environment.

Key takeaway

For AI Scientists and Robotics Engineers developing autonomous multi-agent systems, this research suggests focusing on individual sensorimotor capabilities and constraints rather than prescribing explicit interaction rules. Your designs should prioritize realistic perception, internal state estimation, and physical action limits, as these biologically interpretable parameters drive emergent collective behaviors and their transitions. Consider how varying field of view, sensory noise, and motor agility in your agents will directly influence group dynamics and cohesion, potentially leading to more robust and adaptable collective intelligence.

Key insights

Collective behavior emerges from individual sensorimotor regularities, not abstract interaction forces.

Principles

• Sensorimotor parameters govern collective behavioral transitions.
• Memory is critical for group cohesion under narrow fields of view.
• Bearing and apparent size noise degrade collective structure differently.

Method

The AICON framework uses recursive estimators for probabilistic beliefs and active interconnections for differentiable functional dependencies, propagating gradients from a single cost function (minimizing deviation from desired social distance) to actions.

In practice

• Simulate collective behavior using sensorimotor parameters.
• Vary field of view, sensory noise, and turning agility.
• Incorporate memory for robust group cohesion.

Topics

• Mechanistic Models
• Sensorimotor Regularities
• Active InterCONnect
• Collective Motion
• Behavioral Transitions

Best for: AI Scientist, Robotics Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.