Research
My work lies at the intersection of robot learning, optimal control, and physics. I develop control architectures and learning algorithms that are informed by the physical dynamics of robot bodies and their interactions with the environment. I am passionate about deploying this research on high-performance hardware, from microrobots to autonomous racecars, with a focus on real-time applications of reinforcement learning, model-predictive control, and methods from statistical physics.
Online Learning on High-Performance Hardware
Deploying learning algorithms aboard high-performance hardware presents substantial challenges. Systems such as autonomous racecars are costly and pose risk to their environments even during nominal operation. In my work, I develop online robot learning methodologies amenable to safety-critical deployment.
Physical Active Learning
The physical properties of embodied agents can have a substantial impact on learning and control outcomes. The physics of sensors, the structure of the environment, and correlations introduced by the dynamics of robot bodies are all crucial to task-capability. In my work, I design algorithms that exploit and adapt to the physics of agents in real-time.
Related Publications
Physical state exploration for reinforcement learning from scratch
Allison Pinosky, Thomas A. Berrueta, Olivia Li, Todd D. Murphey
Proceedings of the International Conference on Automation Science and Engineering (CASE), 2025.
Maximum diffusion reinforcement learning
Thomas A. Berrueta, Allison Pinosky, Todd D. Murphey
Nature Machine Intelligence, vol. 6, no. 5, pp. 504-514, 2024.
Active learning in robotics: A review of control principles
Annalisa T. Taylor, Thomas A. Berrueta, Todd D. Murphey
Mechatronics, vol. 77, 102576, 2021.
Optimization of Embodied Intelligence
The embodied intelligence of biological organisms supports and enhances their cognitive abilities by offloading computational burdens to the material make-up of agents. There is a need for task-speficic design principles capable of accounting for and realizing physical intelligence in robotic systems. My work explores novel design principles for exploiting synergies between the material and information-processing capabilities of robotic agents.
Related Publications
Materializing autonomy in soft robots across scales
Thomas A. Berrueta, Todd D. Murphey, Ryan L. Truby
Advanced Intelligent Systems, vol. 6, no. 2, 2300111, 2023.
Emergent microrobotic oscillators via asymmetry-induced order
Thomas A. Berrueta, Jing Fan Yang, Allan M. Brooks, Albert Tianxiang Liu, Ge Zhang, David Gonzalez-Medrano, Sungyun Yang, Volodymyr B. Koman, Pavel Chvykov, Lexy N. LeMar, Marc Z. Miskin, Todd D. Murphey, Michael S. Strano
Nature Communications, vol. 13, 5734, 2022.
Memristor circuits for colloidal robotics: Temporal access to memory, sensing, and actuation
Jing Fan Yang, Albert Tianxiang Liu, Thomas A. Berrueta, Ge Zhang, Allan M. Brooks, Volodymyr B. Koman, Sungyun Yang, Xun Gong, Todd D. Murphey, Michael S. Strano
Advanced Intelligent Systems, vol. 4, no. 4, 2100205, 2022.
Control of Robot Collectives
Interactions between agents in a collective can form the basis of physical intelligence and emergent capabilities. By self-organizing their behavior from fully-distributed and simplistic interactions, collectives in nature are capable of robustly surpassing the computational limitations of individuals. By taking advantage of emergence and self-organization, my work develops novel control principles for robot collectives.
Related Publications
Low rattling: A predictive principle for self-organization in active collectives
Pavel Chvykov, Thomas A. Berrueta, Akash Vardhan, William Savoie, Alexander Samland, Todd D. Murphey, Kurt Wiesenfeld, Daniel I. Goldman, Jeremy L. England
Science, vol. 371, no. 6524, 2021.
Information requirements of collision-based micromanipulation
Thomas A. Berrueta, Alexandra Q. Nilles, Ana Pervan, Todd D. Murphey, Steven M. LaValle
Proceedings of the Workshop on the Algorithmic Foundations of Robotics (WAFR), 2020.
A robot made of robots: Emergent transport and control of a smarticle ensemble
William Savoie, Thomas A. Berrueta, Zachary Jackson, Ana Pervan, Ross Warkentin, Shengkai Li, Todd D. Murphey, Kurt Wiesenfeld, Daniel I. Goldman
Science Robotics, vol. 4, no. 34, 2019.