Abstract: The ability to understand physical interaction among objects lies at the core of human cognition; it is also essential in building intelligent machines that see and manipulate objects in the real world. In this talk, I’ll first discuss cognitive models that explain human physical reasoning via an “intuitive” physical simulation engine. Then, we present our work on using deep learning (esp. graph networks) to. approximate physical simulation. Our recent findings suggest that (i) learning systems can approximate physical interaction at various granularities, ranging from rigid bodies to deformable shapes to fluids, (ii) the learned physical model implicitly encodes the physical object properties that govern the interaction, and (iii) incorporating physics explicitly into learning systems leads to improvement in both performance and data-efficiency for robot manipulation. I’ll also discuss how learned physics engines may be integrated with visual perception modules for scene understanding.
Bio: Jiajun Wu is an Assistant Professor of Computer Science at Stanford University, working on computer vision, machine learning, and computational cognitive science. Before joining Stanford, he was a Visiting Faculty Researcher at Google Research. He received his PhD in Electrical Engineering and Computer Science at Massachusetts Institute of Technology. Wu’s research has been recognized through the ACM Doctoral Dissertation Award Honorable Mention, the AAAI/ACM SIGAI Doctoral Dissertation Award, the MIT George M. Sprowls PhD Thesis Award in Artificial Intelligence and Decision-Making, the 2020 Samsung AI Researcher of the Year, the IROS Best Paper Award on Cognitive Robotics, and fellowships from Facebook, Nvidia, Samsung, and Adobe.