← Search

Matthew Sivaprakasam

7 accepted papers

2025

BETTY Dataset: A Multi-Modal Dataset for Full-Stack Autonomy

ICRA 2025

We present the BETTY dataset, a large-scale, multi-modal dataset collected on several autonomous racing vehicles, targeting supervised and self-supervised state estimation, dynamics modeling, motion forecasting, perception, and more. Existing large-scale datasets, especially autonomous vehicle datas

Cited by 1SourcecodeScholar
2025

SALON: Self-supervised Adaptive Learning for Off-road Navigation

ICRA 2025

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 scenarios. While learned methods using hand labels or self-supervised data improve generalizability, they often require a

Cited by 10SourceScholar
2024

TartanDrive 2.0: More Modalities and Better Infrastructure to Further Self-Supervised Learning Research in Off-Road Driving Tasks

ICRA 2024poster

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, we collected seven hours of data at speeds of up to 15m/s wit…

Cited by 21SourceScholar
2024

Velociraptor: Leveraging Visual Foundation Models for Label-Free, Risk-Aware Off-Road Navigation

CoRL 2024poster

Traversability analysis in off-road regimes is a challenging task that requires understanding of multi-modal inputs such as camera and LiDAR. These measurements are often sparse, noisy, and difficult to interpret, particularly in the off-road setting. Existing systems are very engineering-intensive,…

Cited by 2SourceScholar
2023

Learning Risk-Aware Costmaps via Inverse Reinforcement Learning for Off-Road Navigation

ICRA 2023poster

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 sensory observations using corpora of expert driving data. However,…

Cited by 31SourceScholar
2022

TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models

ICRA 2022poster

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 in diverse terrains. To the authors' knowledge, this is the la…

Cited by 62SourcecodeScholar
2021

Improving Off-road Planning Techniques with Learned Costs from Physical Interactions

ICRA 2021poster

Autonomous ground vehicles have improved greatly over the past decades, but they still have their limitations when it comes to off-road environments. There is still a need for planning techniques that effectively handle physical interactions between a vehicle and its surroundings. We present a metho…

Cited by 20SourceScholar