In the Interactive and Emergent Autonomy Lab, our research focuses on computational methods in data-driven control, information theory in physical systems, and embodied intelligence. We investigate how both autonomous systems and biological systems interact with their environments (and, in some cases, with each other) to learn and improve their behaviors. This work often involves mathematical modeling, development of new mathematical tools, algorithmic implementation and programming, and experimentation.
November 7, 2019 New Lab Member
November 7, 2019 Taosha Wins Best Paper at IROS 2019
September 18, 2019 "A robot made of robots..." published in Science Robotics
August 22, 2019 MURI Meeting on Algorithmic Matter and Emergent Behavior
June 22, 2019 Giorgos presents at RSS
May 20, 2019 DARPA award recieved
April 12, 2019 MURI award
January 9, 2019 Ola's Masters Thesis Defense and PhD Prospectus
December 9, 2018 Ian, Taosha, and Ana present at WAFR
September 27, 2018 New Lab Members
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
Ergodicity reveals assistance and learning in physical human robot interaction
K. Fitzsimons, A. M. Acosta, J. Dewald, and T. D. Murphey
Science Robotics, vol. 4, no. 29, 2019.
For a video and a freely accessible link to the paper, click here.
Active learning of dynamics for data-driven control using Koopman operators
I. Abraham and T. D. Murphey
IEEE Transactions on Robotics, 2019.
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