Dr. Petr Kellnhofer's work "Gaze360: Physically Unconstrained Gaze Estimation in the Wild" was accepted to ICCV 2019. In this paper, we present Gaze360, a large-scale gaze-tracking dataset and method for robust 3D gaze estimation in unconstrained images. Our proposed 3D gaze model extends existing models to include temporal information and to directly output an estimate of gaze uncertainty. Finally, we demonstrate an application of our model for estimating customer attention in a supermarket setting. This paper was was in collaboration with Professor Antonio Torralba's group and Toyota Research Institute.
Andrew Spielberg's paper "Learning-In-The-Loop Optimization: End-To-End Control And Co-Design of Soft Robots Through Learned Deep Latent Representations" has been accepted to NeurIPS 2019 recently. This paper tackles the problem of controlling soft robots, which is very challenging due to their infinite degrees of freedom. Our solution marries hybrid particle-grid-based simulation with deep, variational convolutional autoencoder architectures that can capture salient features of robot dynamics with high efficacy, and we demonstrate our dynamics-aware feature learning algorithm on both 2D and 3D soft robots.