Can a machine design, adapt, and improve other machines—safely, reliably, and without constant human oversight? Answering this question is at the heart of Yorie Nakahira’s research on next-generation autonomous systems.
Nakahira, assistant professor of electrical and computer engineering, has received a Young Investigator Award from the Japan Science and Technology Agency (JST) for developing a machine that builds autonomous systems.
Adaptation is essential because real-world environments are constantly changing. Robots often operate with limited data, limited memory, and limited computing power, making it unrealistic to train them once and expect safe performance forever.
To address this, Nakahira explores sequential fine-tuning, where systems learn gradually as new information becomes available. This enables continual adaptation rather than reliance on a single, massive training dataset.
However, adaptation introduces its own risks: the model can forget past skills or become overly confident. Nakahira’s work also emphasizes uncertainty awareness—teaching systems to recognize when they are unsure and to act cautiously in those moments.
