Stanford AA289/ENGR319 - Robotics and Autonomous Systems Seminar - Stanford Seminar - Multi-Sensory Neural Objects: Modeling, Inference, and Applications in Robotics

October 14, 2022 Jiajun Wu of Stanford University In the past two years, neural representations for objects and scenes have demonstrated impressive performance on graphics and vision tasks, particularly on novel view synthesis, and have gradually gained attention from the robotics community due to their potential robotic applications. In this talk, I'll present our recent efforts in building neural representations that are object-centric and multi-sensory---two properties that are essential for flexible, efficient, and generalizable robot manipulation. I'll focus on four aspects: technical innovations in building such representations, advances in scaling them up in the form of a multi-sensory neural object dataset, methods for inferring category-agnostic neural object representations and their parameters (SysID) from unlabeled visual data, and systems that adopt these representations for robotic manipulation. About the speaker: I am an Assistant Professor of Computer Science at Stanford University, affiliated with the Stanford Vision and Learning Lab (SVL) and the Stanford AI Lab (SAIL). I study machine perception, reasoning, and interaction with the physical world, drawing inspiration from human cognition. Here is some information for prospective students and visitors. Before joining Stanford, I was a Visiting Faculty Researcher at Google Research, New York City, working with Noah Snavely. I finished my PhD at MIT, advised by Bill Freeman and Josh Tenenbaum, and my undergraduate degrees at Tsinghua University, working with Zhuowen Tu. https://jiajunwu.com/ #robotics