In 2020, the team solved this by using liquid neural networks with 19 nodes, so 19 neurons plus a small perception module could drive a car. A differential equation describes each node of that system.

Liquid neural networks offer “an elegant and compact alternative,” said Ken Goldberg, a roboticist at the University of California, Berkeley. He added that experiments are already showing that these ...