2 citations · 6 across the 9 of their papers we have counts for
10 papers
Structured Perception Representations for Off-road Dynamics Learning
Micah Nye, Samuel Triest, Matthew Sivaprakasam +3
Autonomously navigating off-road terrain at high speed demands accurate control, which often relies on dynamics models that capture complex interactions between the robot and terra…
Distilling Global Traversability Priors for Image-based Affordance Prediction in Off-road Environments
Matthew Sivaprakasam, Samuel Triest, Micah Nye +5
Standard methods for autonomous navigation in unstructured terrain are prone to myopic behaviors in long-horizon scenarios. The use of metric maps built from LiDAR or cameras provi…
BETTY Dataset: A Multi-modal Dataset for Full-Stack Autonomy
Micah Nye, Ayoub Raji, Andrew Saba +9
We present the BETTY dataset, a large-scale, multi-modal dataset collected on several autonomous racing vehicles, targeting supervised and self-supervised state estimation, dynamic…
SALON: Self-supervised Adaptive Learning for Off-road Navigation
Matthew Sivaprakasam, Samuel Triest, Cherie Ho +5
Autonomous robot navigation in off-road environments presents a number of challenges due to its lack of structure, making it difficult to handcraft robust heuristics for diverse sc…
Fast and Modular Autonomy Software for Autonomous Racing Vehicles
Andrew Saba, Aderotimi Adetunji, Adam Johnson +27
Autonomous motorsports aim to replicate the human racecar driver with software and sensors. As in traditional motorsports, Autonomous Racing Vehicles (ARVs) are pushed to their han…
Deep Bayesian Future Fusion for Self-Supervised, High-Resolution, Off-Road Mapping
Shubhra Aich, Wenshan Wang, Parv Maheshwari +6
High-speed off-road navigation requires long-range, high-resolution maps to enable robots to safely navigate over different surfaces while avoiding dangerous obstacles. However, du…
TartanDrive 2.0: More Modalities and Better Infrastructure to Further Self-Supervised Learning Research in Off-Road Driving Tasks
Matthew Sivaprakasam, Parv Maheshwari, Mateo Guaman Castro +6
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…
PIAug -- Physics Informed Augmentation for Learning Vehicle Dynamics for Off-Road Navigation
Parv Maheshwari, Wenshan Wang, Samuel Triest +5
Modeling the precise dynamics of off-road vehicles is a complex yet essential task due to the challenging terrain they encounter and the need for optimal performance and safety. Re…
Learning Risk-Aware Costmaps via Inverse Reinforcement Learning for Off-Road Navigation
Samuel Triest, Mateo Guaman Castro, Parv Maheshwari +3
The process of designing costmaps for off-road driving tasks is often a challenging and engineering-intensive task. Recent work in costmap design for off-road driving focuses on tr…
TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models
Samuel Triest, Matthew Sivaprakasam, Sean J. Wang +3
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 modifi…