RE•WORK Deep Learning Summit

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.