Drones Navigate Unseen Environments With Liquid Neural Networks

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a method for robust flight navigation agents to master vision-based fly-to-target tasks in intricate, unfamiliar environments. The liquid neural networks, which can continuously adapt to new data inputs, showed prowess in making reliable decisions in unknown domains like forests, urban landscapes, and environments with added noise, rotation, and occlusion. These adaptable models could enable potential real-world drone applications like search and rescue, delivery, and wildlife monitoring.