Interactive & Emergent Autonomy Lab


Active Learning and Data-Driven Control Active Perception in Human-Swarm Collaboration Algorithmic Matter and Emergent Computation Control for Nonlinear and Hybrid Systems Cyber Physical Systems in Uncertain Environments Information Maximizing Clinical Diagnostics Reactive Learning in Underwater Exploration Robot-Assisted Rehabilitation Software-Enabled Biomedical Devices

Algorithmic Matter and Emergent Computation

Smart, active particles (called smarticles) are capable of locomotion in a group

Algorithmic Matter

Portrayal of simple, autonomous agents foraging for energy and using their environment to regroup

We define algorithmic, or active, matter as ensembles of particles that leverage their physical characteristics and their interaction with the environment, using limited computational resources, communication, and memory to achieve complex tasks. Specifically, we are working toward being able to:

predict physical and computational requirements for emergent computation,
• determine what non-equilibrium characteristics cause these systems to evolve towards the desired emergent behavior,
design efficient collective computational systems to achieve specific task-oriented goals.

The overarching goal of this work is to develop and test experimental, simulation, and theoretical frameworks to discover the fundamental principles that would allow one to synthesize emergent behavior in self-organizing systems.


Tommy Berrueta (Ph.D. Student)
Ana Pervan (Ph.D. Student)
Annalisa Taylor (Ph.D. Student)
Karalyn Baird (Undergraduate Student)


Jeremy England, GlaxoSmithKline Artificial Intelligence
Dan Goldman, Georgia Institute of Technology
Dana Randall, Georgia Institute of Technology
Andrea Richa, Arizona State University
Michael Strano, Massachussets Institute of Technology


Low Rattling: A Predictive Principle for Self-Organization in Active Collectives
P. Chvykov, T. A. Berrueta, A. Vardhan, W. Savoie, A. Samland, T. D. Murphey, K. Wiesenfeld, D. I. Goldman, and J. L. England
Science, vol. 371, no. 6524, 2021. Article

Algorithmic Design for Embodied Intelligence in Synthetic Cells
A. Pervan and T. D. Murphey
IEEE Transactions on Automation Science and Engineering (T-ASE), 2020. PDF

Bayesian Particles on Cyclic Graphs
A. Pervan and T. D. Murphey
IEEE Int. Conf. on Intelligent Robots and Systems (IROS), 2020. PDF

Autoperforation of Two-Dimensional Materials to Generate Colloidal State Machines Capable of Locomotion
A. T. Liu, J. F. Yang, L. N. LeMar, G. Zhang, A. Pervan, T. D. Murphey, M. Strano
Faraday Discussions, Royal Society of Chemistry, 2020. PDF

A robot made of robots: emergent transport and control of a smarticle ensemble
W. Savoie, T. A. Berrueta, Z. Jackson, A. Pervan, R. Warkentin, S. Li, T. D. Murphey, K. Wiesenfeld, and D. I. Goldman
Science Robotics, vol. 4, no. 34, 2019. Paper

Low complexity control policy synthesis for cyber-free robot design
A. Pervan and T. D. Murphey
Workshop on the Algorithmic Foundations of Robotics (WAFR), 2018. PDF

Algorithmic materials: Embedding computation within material properties for autonomy
A. Pervan and T. D. Murphey
Robotic Systems and Autonomous Platforms: Advances in Materials and Manufacturing, Elsevier, 2018. Eds. M. Strano and S. Walsh. PDF


This project is funded by the Army Research Office MURI: Formal Foundations of Algorithmic Matter and Emergent Computation.