Many autonomous systems forecast aspects of the future in order to aid decision-making.
For example, self-driving vehicles and robotic manipulation systems often forecast future …

Building models capable of generating structured output is a key challenge for AI and
robotics. While generative models have been explored on many types of data, little work has …

A low‐cost and accurate positioning solution is significant for the massive deployment of
fully autonomous driving vehicles (ADV). Conventional mechanical LiDAR has proven its …

Predicting the future is a crucial first step to effective control, since systems that can predict
the future can select plans that lead to desired outcomes. In this work, we study the problem …

In recent years, mobile robots have become increasingly frequent in daily life applications,
such as cleaning, surveillance, support for the elderly and people with disabilities, as well as …

In autonomous driving, there has been an explosion in the use of deep neural networks for
perception, prediction and planning tasks. As autonomous vehicles (AVs) move closer to …

Annotating objects with 3D bounding boxes in LiDAR pointclouds is a costly human driven
process in an autonomous driving perception system. In this paper, we present a method to …

The classical “modular and cascaded” autonomy stack (object detection, then tracking,
trajectory prediction, then motion planning and control) has been widely used in industry for …