Beyond Supervised Driving
Crowd-sourced steering does not sound as appealing as automated driving. We need to go beyond supervised learning for automated driving, including for computer vision problems seeing great progress with strong supervision today. First, we will motivate exciting scientific problems that have huge implications in the research and development of long-term large-scale autonomous robots, such as unsupervised domain adaptation, self-supervised learning, and robustness to edge cases. Second, we will talk about the robotics system perspective, especially end-to-end vs modular design and human-robot interaction. Finally, we will describe some of TRI's related research directions, especially around world-scale cloud robotics. In particular, we will discuss recent related results obtained in the ML team at TRI on state-of-the-art methods for self-supervised depth and pose prediction from monocular imagery, end-to-end panoptic segmentation, and large scale distributed deep learning on GPUs in the cloud.
Adrien Gaidon is the Manager of the Machine Learning team and a Senior Research Scientist at the Toyota Research Institute (TRI) in Los Altos, CA, USA, working on open problems in world-scale learning for autonomous driving. He received his PhD from Microsoft Research - Inria Paris in 2012 and has over a decade of experience in academic and industrial Computer Vision, with over 27 publications, top entries in international Computer Vision competitions, multiple best reviewer awards, international press coverage for his work on Deep Learning with simulation, and was a guest editor for the International Journal of Computer Vision. You can find him on linkedin (https://www.linkedin.com/in/adrien-gaidon-63ab2358/) and Twitter (@adnothing).