'That was the first time I thought, ‘this actually might be pretty powerful stuff’. It was pretty impressive to me.'
. These included fly-to-target tasks, stress tests, target rotation, and dynamic target tracking.
The LNN was first taught to identify a red chair, leading the drone to recognize and fly toward the chair from distances as far as 145 feet . “I think that was the first time I thought, ‘this actually might be pretty powerful stuff’,” Makram Chahine, a graduate researcher at MIT and co-author said.Test results showed LNN-based architectures to consistently outperform other models, even clocking a high success rate of over 90% in fly-to-target tasks. Further compelling evidence was recorded in range, rotation, and occlusion robustness tests and dynamic target tracking tasks.
Brain-inspired networks showed less drifting from their trajectories than traditional neural networks.While it is too early to pinpoint reasons leading to the high accuracy of liquid networks, researchers hypothesize it relates to the ability to understand causality. “We want to create something that is understandable, controllable. But right now we are far away from that. Everything that we do as a robotics and machine learning lab is [for] all-around safety and deployment of AI in a safe and ethical way in our society, and we really want to stick to this mission and vision that we have,” Hasani says, talking about safety and over-automation concerns.
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