Autonomous mobility tasks such as lastmile delivery require reasoning about operator
indicated preferences over terrains on which the robot should navigate to ensure both robot …

Autonomous navigation in unstructured vegetated environments remains an open
challenge. To successfully operate in these settings, ground vehicles must assess the …

We introduce a self-supervised method for systematically choosing traversable terrain while
autonomously navigating a vehicle to a goal position in an unknown off-road environment …

Reliable estimation of terrain traversability is critical for the successful deployment of
autonomous systems in wild, outdoor environments. Given the lack of large-scale annotated …

Lane graph estimation is a long-standing problem in the context of autonomous driving.
Previous works aimed at solving this problem by relying on large-scale, hand-annotated …

Legged robots can travel through complex scenes via dynamic foothold adaptation.
However, it remains a challenging task to efficiently utilize the dynamics of robots in cluttered …

Autonomous navigation in unstructured vegetated environments remains an open
challenge. To successfully operate in these settings, autonomous ground vehicles (AGVs) …

We introduce an approach to train neural network models for visual object localization using
a small training set, labeled with ground truth object positions and a large unlabeled one …

The achievement of robot autonomy has environmental perception as a prerequisite. The
hazards rendered from uneven, soft and slippery terrains, which are generally named non …

In this article, we present the circular accessible depth (CAD), a robust traversability
representation for an unmanned ground vehicle (UGV) to learn traversability in various …