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Physics-informed digital twin and onboard control of a brainbot for intelligent active matter

I. Mammadli1, P. Shrestha2, J. Pande3,*, F. Novkoski1,†, S. Mohapatra1, M. Noirhomme4, A. Maier2, N. Vandewalle4, and A.-S. Smith1,5

  • *Contact author: jayant.pande@flame.edu.in
  • †Contact author: filip.novkoski@fau.de
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Phys. Rev. Research 8, 033164 – Published 10 August, 2026

DOI: https://doi.org/10.1103/rd1t-h2v6

Abstract

Establishing adaptive particles that sense their state, anticipate their evolution, and compute control inputs onboard has been a major challenge in nonequilibrium physics. We address this challenge by realizing an autonomous brainbot, building on a recently developed programmable bristlebot. First, we construct a physics-informed digital twin of the device, based on a kinematic model that reproduces measured trajectory statistics and generates long, statistically faithful synthetic trajectories. The kinematics forms the foundation for implementing onboard model predictive control, enabling autonomous trajectory tracking, demonstrated by accurate execution of a nontrivial target path. This provides a proof of principle for a brainbot that senses its state, predicts its evolution, and computes control inputs onboard, unlike conventional active particles with fixed motility, thereby transforming the brainbot into an agentic physical entity. By integrating physical modeling, data-driven parameter identification, and control into a unified framework, our approach provides a scalable platform for machine-learning-enabled multiagent studies and lays the groundwork for intelligent, adaptive active matter.

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Phys. Rev. Research 8, 033164– Published 10 August, 2026
  • Received 30 March 2026
  • Accepted 16 June 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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