Estimating terrain traversability in off-road environments requires reasoning about complex
interaction dynamics between the robot and these terrains. However, it is challenging to …

We present TartanDrive, a large scale dataset for learning dynamics models for off-road
driving. We collected a dataset of roughly 200,000 off-road driving interactions on a modified …

Most conventional wheeled robots can only move in flat environments and simply divide
their planar workspaces into free spaces and obstacles. Deeming obstacles as non …

Most autonomous navigation systems assume wheeled robots are rigid bodies and their 2D
planar workspaces can be divided into free spaces and obstacles. However, recent wheeled …

We present TartanDrive 2.0, a large-scale off-road driving dataset for self-supervised
learning tasks. In 2021 we released TartanDrive 1.0, which is one of the largest datasets for …

Wheeled robots have recently demonstrated superior mechanical capability to traverse
vertically challenging terrain (eg, extremely rugged boulders comparable in size to the …

Autonomous offroad driving is essential for applications like emergency rescue, military
operations, and agriculture. Despite progress, systems struggle with high-speed vehicles …

While engaging with the unfolding revolution in autonomous driving, a challenge presents
itself, how can we effectively raise awareness within society about this transformative trend …

Off-road navigation on vertically challenging ter-rain, involving steep slopes and rugged
boulders, presents significant challenges for wheeled robots both at the planning level to …

We present VertiEncoder, a self-supervised representation learning approach for robot
mobility on vertically challenging terrain. Using the same pre-training process, VertiEncoder …