Samuel Triest
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View article: UNRealNet: Learning Uncertainty-Aware Navigation Features from High-Fidelity Scans of Real Environments
UNRealNet: Learning Uncertainty-Aware Navigation Features from High-Fidelity Scans of Real Environments Open
Traversability estimation in rugged, unstructured environments remains a challenging problem in field robotics. Often, the need for precise, accurate traversability estimation is in direct opposition to the limited sensing and compute capa…
View article: TartanDrive 2.0: More Modalities and Better Infrastructure to Further Self-Supervised Learning Research in Off-Road Driving Tasks
TartanDrive 2.0: More Modalities and Better Infrastructure to Further Self-Supervised Learning Research in Off-Road Driving Tasks Open
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 off-road terrain. As a follow-up to our original dataset, …
View article: Learning Risk-Aware Costmaps via Inverse Reinforcement Learning for Off-Road Navigation
Learning Risk-Aware Costmaps via Inverse Reinforcement Learning for Off-Road Navigation Open
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 training deep neural networks to predict costmaps from sensor…
View article: How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability
How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability Open
Estimating terrain traversability in off-road environments requires reasoning about complex interaction dynamics between the robot and these terrains. However, it is challenging to create informative labels to learn a model in a supervised…
View article: TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models
TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models Open
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 Yamaha Viking ATV with seven unique sensing modalities i…
View article: Rough Terrain Navigation Using Divergence Constrained Model-Based Reinforcement Learning
Rough Terrain Navigation Using Divergence Constrained Model-Based Reinforcement Learning Open
Autonomous navigation of wheeled robots in rough terrain environ-ments has been a long standing challenge. In these environments, predicting therobot’s trajectory is challenging due to the complexity of terrain interactions andthe divergen…