Li Fei-Fei

TL;DR: This work presents the first comprehensive benchmark for training and evaluating Interactive Navigation solutions, and presents and evaluates multiple learning-based baselines in Interactive Gibson Benchmark, and provides insights into regimes of navigation with different trade-offs between navigation, path efficiency and disturbance of surrounding objects.

TL;DR: The Decompositional Disentangled Predictive Auto-Encoder (DDPAE) is proposed, a framework that combines structured probabilistic models and deep networks to automatically decompose the high-dimensional video that the authors aim to predict into components, and disentangle each component to have low-dimensional temporal dynamics that are easier to predict.

TL;DR: In this article, a crowdsourcing platform for high quality 6-DoF trajectory based teleoperation through the use of widely available mobile devices (e.g. iPhone) is introduced.

TL;DR: Self-supervision is used to learn a compact and multimodal representation of the authors' sensory inputs, which can then be used to improve the sample efficiency of the policy learning of self-supervised learning algorithms.

TL;DR: In this paper, a deep learning approach to category-level 6D object pose tracking on RGB-D data is presented. But their method is limited to object instances of known object categories such as bowls, laptops, and mugs.