Engineers are on a failure-finding mission
From vehicle collision avoidance to airline scheduling systems to power supply grids, many of the services we rely on are managed by computers. As these autonomous systems grow in complexity and ubiquity, so too could the ways in which they fail.
Now, MIT engineers have developed an approach that can be paired with any autonomous system, to quickly identify a range of potential failures in that system before they are deployed in the real world. What’s more, the approach can find fixes to the failures, and suggest repairs to avoid system breakdowns.
The team has shown that the approach can root out failures in a variety of simulated autonomous systems, including a small and large power grid network, an aircraft collision avoidance system, a team of rescue drones, and a robotic manipulator. In each of the systems, the new approach, in the form of an automated sampling algorithm, quickly identifies a range of likely failures as well as repairs to avoid those failures.
The new algorithm takes a different tack from other automated searches, which are designed to spot the most severe failures in a system. These approaches, the team says, could miss subtler though significant vulnerabilities that the new algorithm can catch.
“In reality, there’s a whole range of messiness that could happen for these more complex systems,” says Charles Dawson, a graduate student in MIT’s Department of Aeronautics and Astronautics. “We want to be able to trust these systems to drive us around, or fly an aircraft, or manage a power grid. It’s really important to know their limits and in what cases they’re likely to fail.”
Dawson and Chuchu Fan, assistant professor of aeronautics and astronautics at MIT, are presenting their work this week at the Conference on Robotic Learning.
Sensitivity over adversaries
In 2021, a major system meltdown in Texas got Fan and Dawson thinking. In February of that year, winter storms rolled through the state, bringing unexpectedly frigid temperatures that set off failures across the power grid. The crisis left more than 4.5 million homes and businesses without power for multiple days. The system-wide breakdown made for the worst energy crisis in Texas’ history. More