Interactive & Emergent Autonomy Lab

Tommy Berrueta

Research

I am interested in the study of complex, often multi-agent, robotic systems and their emergent behaviors. I focus on the development of algorithmic tools for learning to identify and control such emergent behavior in order to harness it towards achieving challenging objectives. My research is at the intersection of robotics, machine learning, information theory, and statistical physics.

Projects

Active Perception in Human-Swarm Collaboration
Algorithmic Matter and Emergent Computation
Software-Enabled Biomedical Devices

Education

B.S. Engineering, Harvey Mudd College, 2017

Awards and Honors

Harvey S. Mudd Scholarship, 2013-2017
Microsoft Ada Lovelace Fellowship Finalist, 2019

Teaching

Grader for Machine Dynamics (ME 314), Fall 2018

Other Interests

Lover of music listening, playing and composition



Publications

Information Requirements of Collision-Based Micromanipulation
A. Q. Nilles, A. Pervan, T. A. Berrueta, T. D. Murphey, S. M. LaValle
Workshop on the Algorithmic Foundations of Robotics (WAFR), 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

Dynamical system segmentation for information measures in motion
T. Berrueta, A. Pervan, K. Fitzsimons, and T. D. Murphey
IEEE Robotics and Automation Letters, vol. 4, no. 1, pp. 169–176, 2019. PDF

Data-driven gait segmentation for walking assistance in a lower-limb assistive device
A. Kalinowska, T. Berrueta, A. Zoss, and T. D. Murphey
IEEE Int. Conf. on Robotics and Automation (ICRA), 2019. PDF