Shuran Song, Columbia University: Learning Meets Gravity: Robots that Embrace Dynamics and Pixels

Join the Center for Robotics and Biosystems (CRB) May 2023 Speaker
Speaker: Shuran Song, Assistant Professor of Computer Science and Director of the Columbia Artificial Intelligence and Robotics (CAIR) Lab at Columbia University
Title: Learning Meets Gravity: Robots that Embrace Dynamics and Pixels
Date and Time: Friday, May 5 at 12:00 p.m. CT
Location: Tech B211 and Zoom
Abstract: Despite the incredible capabilities (speed and repeatability) of our hardware today, many robot manipulators are deliberately programmed to avoid dynamics - moving slow enough so they can adhere to quasi-static assumptions of the world. In contrast, people frequently (and subconsciously) make use of dynamic phenomena to manipulate everyday objects - from unfurling blankets, to tossing trash - to improve efficiency and physical reach range. These abilities are made possible by an intuition of physics, a cornerstone of intelligence. How do we impart the same on robots?
Modeling the complex dynamics of unseen objects from pixels is challenging. However, by tightly integrating perception and action, we show that it is possible to relax the need for accurate dynamical models. Thereby allowing robots to (i) learn dynamic skills for complex objects, (ii) adapt to new scenarios using visual feedback, and (iii) use their dynamic interactions to improve their understanding of the world. By changing the way we think about dynamics - from avoiding it to embracing it - we can simplify a number of classically challenging problems, leading to new robot capabilities
Bio: Shuran Song is an Assistant Professor in the Department of Computer Science and Director of the Columbia Artificial Intelligence and Robotics (CAIR) Lab at Columbia University. Before that, she received her Ph.D. in Computer Science at Princeton University, Bachelor of Engineering at Hong Kong University of Science and Technology (HKUST). Her research interests lie at the intersection of computer vision and robotics. Song’s research has been recognized through several awards including the Best Paper Awards at RSS’22 and T-RO’20, Best System Paper Awards at CoRL’21, RSS’19, and finalist at RSS, ICRA, CVPR, and IROS. She is also a recipient of the NSF Career Award, Sloan Foundation fellowship as well as research awards from Microsoft, Toyota Research, Google, Amazon, and JP Morgan. To learn more about Shuran’s work please visit: www.cs.columbi...