Drones may better navigate unfamiliar surroundings with the help of liquid neural networks
MIT points out that deep learning systems can flounder when it comes to understanding causality and can't always adapt to different environments or conditions. That poses a problem for drones, which have to be able to react quickly to obstacles.
"Our experiments demonstrate that we can effectively teach a drone to locate an object in a forest during summer, and then deploy the model in winter, with vastly different surroundings, or even in urban settings with varied tasks such as seeking and following,” Computer Science and Artificial Intelligence Laboratory director, MIT professor and paper co-author Daniela Rus said in a statement. “This adaptability is made possible by the causal underpinnings of our solutions.
The researchers trained their system on data captured by a human pilot. This enabled them to account for the pilot's ability to use their navigation skills in new environments that have undergone significant changes in conditions and scenery. In testing the liquid neural nets, the researchers found that drones were able to track moving targets, for instance.
“Robust learning and performance in out-of-distribution tasks and scenarios are some of the key problems that machine learning and autonomous robotic systems have to conquer to make further inroads in society critical applications,” says Alessio Lomuscio, PhD, professor of AI Safety at Imperial College London. “In this context the performance of liquid neural networks, a novel brain-inspired paradigm developed by the authors at MIT, reported in this study is remarkable.
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