Improving data efficiency and accessibility for general robotic manipulation

Abstract: How can data-driven approaches endow robots with diverse manipulative skills and robust performance in unstructured environments? Despite recent progress, many open questions remain in this area, such as: (1) How can we define and model the data distribution for robotic systems? (2) In light of data scarcity, what strategies can algorithms employ to enhance performance? (3) What is the best way to scale up robotic data collection? In this talk, Hao-Shu Fang will share his research on enhancing the efficiency of robot learning algorithms and democratizing access to large-scale robotic manipulation data. He will also discuss several open questions in data-driven robotic manipulation, offering insights to the challenges posed.

Bio: Hao-Shu Fang is a postdoctoral researcher collaborating with Pulkit Agrawal and Edward Adelson. His research focuses on general robotic manipulation. Recently, he has been investigating how to integrate visual-tactile perception for improved manipulation and how to train a multi-task robotic foundation behavioral model.