Abstract: Recent work has shown deep learning can significantly improve the prediction of dynamical systems. However, an inability to generalize under distributional shift limits its applicability to the real world. In this talk, I will demonstrate how to principally incorporate relation and symmetry structure in deep learning models to improve generalization. I will showcase their applications to robotic manipulation and vehicle trajectory prediction tasks.

Bio: Rose Yu is an assistant professor at UCSD in CSE and a primary faculty in the AI Group. She is also affiliated with HDSI, CRI and MICS. She does research on ML with an emphasis on large-scale spatiotemporal data. Her work has been used across a variety of use-cases such as dynamic systems, healthcare and physical sciences. Dr. Yu received her PhD from USC and was a postdoc at CalTech. She has already won numerous awards.

Reference:
[1] Deep Imitation Learning for Bimanual Robotic Manipulation
Fan Xie, Alex Chowdhury, Clara De Paolis, Linfeng Zhao, Lawson Wong, Rose Yu
Advances in Neural Information Processing Systems (NeurIPS), 2020
[2] Trajectory Prediction using Equivariant Continuous Convolution
Robin Walters, Jinxi (Leo) Li, Rose Yu
International Conference on Learning Representations (ICLR), 2021