In recent years, the development of robotics and artificial intelligence (AI) systems has been
nothing short of remarkable. As these systems continue to evolve, they are being utilized in …

The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …

We propose a novel knowledge distillation framework for effectively teaching a sensorimotor
student agent to drive from the supervision of a privileged teacher agent. Current distillation …

We propose XVO, a semi-supervised learning method for training generalized monocular
Visual Odometry (VO) models with robust off-the-self operation across diverse datasets and …

Witnessing the impressive achievements of pre-training techniques on large-scale data in
the field of computer vision and natural language processing, we wonder whether this idea …

Human drivers can seamlessly adapt their driving decisions across geographical locations
with diverse conditions and rules of the road, eg, left vs. right-hand traffic. In contrast, existing …

We introduce a novel vision-and-language navigation (VLN) task of learning to provide real-
time guidance to a blind follower situated in complex dynamic navigation scenarios …

Abstract We introduce Motion Diversification Networks a novel framework for learning to
generate realistic and diverse 3D human motion. Despite recent advances in deep …

While behavior cloning has recently emerged as a highly successful paradigm for
autonomous driving humans rarely learn to perform complex tasks such as driving via …

We present a highly scalable self-training framework for incrementally adapting vision-
based end-to-end autonomous driving policies in a semi-supervised manner ie over a …