Learning Meets Gravity: Robots that Embrace Dynamics from Pixels
In this talk, I will discuss how we might enable robots to leverage dynamics for manipulation in unstructured environments. Modeling the complex dynamics of unseen objects from pixels is challenging. However, by tightly integrating perception and action, we show 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 at Columbia University. Before that, she received her Ph.D. in Computer Science at Princeton University, BEng. at 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 Amazon Robotics’18, and finalist at RSS’22, ICRA'20, CVPR'19, RSS’19, and IROS'18. She is also a recipient of the NSF Career Award, as well as research awards from Microsoft, Toyota Research, Google, Amazon, JP Morgan, and Sloan Foundation. To learn more about Shuran’s work please visit: https://www.cs.columbia.edu/~shurans/
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