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 Haptic Languages Information Maximizing Clinical Diagnostics Reactive Learning in Underwater Exploration Robot-Assisted Rehabilitation Software-Enabled Biomedical Devices Vibrissal Responsive Neurons
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.
Example projects include robotic exploration using electrosense, robotic exploration using mechanical contact, human-in-the-loop control, and shared control for rehabilitation/assistive devices. We also work in the field of algorithmic matter, where we develop computational models to enable the design of microrobots with minimal actuation, sensing, and computation.
Much of our work focuses on human-machine systems. In our human-swarm collaboration project, we develop algorithms that enable autonomy to automatically react to human sensory and perception needs to improve situational awareness; this is an example of cyber-physical systems in uncertain environments, where we develop algorithms that help complex cyber-physical systems to anticipate future uncertainty. In collaboration with Northwestern's Physical Therapy and Human Movement Sciences Department, we investigate human-machine interaction in biomedical devices through developing interactive robotic systems that help improve human motor control through physical interaction. Many of our projects involve the development of mathematical models and algorithmic implementations to improve how people and autonomy interact.
Brenna Argall, Northwestern University
Julius Dewald, Northwestern University
Dan Goldman, Georgia Institute of Technology
Mitra Hartmann, Northwestern University
Michael Strano, Massachussets Institute of Technology