中央研究院 資訊科學研究所

Recent development in artificial intelligence and machine learning has remarkably advanced machines’ ability to understand images and videos, comprehend natural languages and speech, and outperform human experts in complex games. However, building intelligent robots that can operate in unstructured environments, manipulate unknown objects, and acquire novel skills – to free humans from tedious or dangerous manual work – remains challenging. My research focuses on developing a robot learning framework that enables robots to acquire long-horizon and complex skills with hierarchical structures, such as furniture assembly and cooking. Specifically, I present an interpretable and generalizable program-guided robot learning framework, which represents desired behaviors as a program and acquires primitive skills for executing desired skills. This talk will discuss a series of projects toward building this framework.

Shao-Hua Sun is an Assistant Professor at National Taiwan University in Electrical Engineering. Prior to joining NTU, he recently completed his Ph.D. in Computer Science at the University of Southern California (USC). Before that, he received his B.S. degree in Electrical Engineering from NTU. His research interests span Machine Learning, Robot Learning, Reinforcement Learning, and Program Synthesis.