Speaker: Prof. Sandeep Chinchali, The University of Texas at Austin
Time: 2:40 p.m.-3:30 p.m.
Abstract: Today’s robotic fleets collect terabytes of rich video and LiDAR data that can be used to continually re-train machine learning (ML) models in the cloud. While these fleets should ideally upload all their data to train robust ML models, this is often infeasible due to prohibitive network bandwidth, data labeling, and cloud costs. In this talk, I will present my group’s papers at CORL 2022 that aim to learn robust perception models from geo-distributed robotic fleets. First, I will present a cooperative data sampling strategy for autonomous vehicles (AVs) to collect a diverse ML training dataset in the cloud. Since the AVs have a shared objective but minimal information about each other’s local data distributions, we can naturally cast cooperative data collection as a mathematical game. I will theoretically characterize the convergence and communication benefits of game-theoretic data sampling and show state-of-the-art performance on standard AV datasets. Then, I will transition to our work on synthesizing robust perception models tailored
to robotic control tasks. The key insight is that today’s methods to train robust perception models are largely task-agnostic – they augment a dataset using random image transformations or adversarial examples targeted at a vision model in isolation. However, I will show that accounting for the structure of
an ultimate robotic task, such as differentiable model predictive control, can improve the generalization of perception models. Finally, I will conclude by tying these threads together into a broader vision on robust, continual learning from networked robotic fleets.
Bio: Sandeep Chinchali is an assistant professor in UT Austin’s ECE department. He completed his PhD in computer science at Stanford and undergrad at Caltech, where he researched at NASA JPL. Previously,
he was the first principal data scientist at Uhana, a Stanford startup working on data-driven optimization of cellular networks, now acquired by VMWare. Sandeep’s research on cloud robotics, edge computing, and 5G was recognized with the Outstanding Paper Award at MLSys 2022 and was a finalist for Best Systems Paper at Robotics: Science and Systems 2019. His group is funded by companies such as Lockheed Martin, Honda, Viavi, Cisco, and Intel and actively collaborates with local Austin startups.