Add to Favourites
To login click here
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. This new breed of machine-learning algorithms captures the causal structure of tasks from high-dimensional, unstructured data, allowing acquired navigation skills to transfer targets seamlessly to new environments.
