Methods used to help robots walk and autonomous cars drive can also help epidemiologists predict the spread of the pandemic.
When the COVID-19 pandemic lockdowns brought an abrupt halt to their research this spring, robotics engineers at Caltech and the University of Michigan took tools that were originally created to help robots to walk and autonomous cars to drive safely and applied them to the development of an epidemiological methodology that accounts for human interventions (like mask mandates and stay-at-home orders).
“I was sitting at home in March, like most of the rest of the country, watching this happen around me and wondering how I could help,” says Aaron Ames, Bren Professor of Mechanical and Civil Engineering and Control and Dynamical Systems, who runs Caltech’s Advanced Mobility (AMBER) Lab.
Digging into the epidemiological models that were being used to model the progression of pandemics, Ames realized that they typically view the progression of infection as something whose dynamics progress autonomously (without the ability to modify their evolution), as opposed to a system whose behavior can be influenced by human actions. Yet human actions—things like physical distancing, shutting down indoor dining, and mandating one-way traffic in buildings—can and do shape the progression of COVID-19.
Ames joined efforts with his colleague Gábor Orosz, associate professor in mechanical engineering at the University of Michigan; postdoctoral researcher Tamás Molnár (then at Michigan and now at Caltech); and Caltech graduate student Andrew Singletary (MS ’19). Together, they constructed a new methodology that treats the epidemiological models as control systems, in which various human interventions are included as “inputs” into the system.
Source: “Robotics Engineers Take on COVID-19”, California Institute of Technology
