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Aniket Datar

10 accepted papers

2025

Dom, cars don't fly! - Or do they? In-Air Vehicle Maneuver for High-Speed Off-Road Navigation

IROS 2025

When pushing the speed limit for aggressive off-road navigation on uneven terrain, it is inevitable that vehicles may become airborne from time to time. During time-sensitive tasks, being able to fly over challenging terrain can also save time, instead of cautiously circumventing or slowly negotiati

Cited by 2SourceScholar
2025

M2P2: A Multi-Modal Passive Perception Dataset for Off-Road Mobility in Extreme Low-Light Conditions

IROS 2025

Long-duration, off-road, autonomous missions require robots to continuously perceive their surroundings regardless of the ambient lighting conditions. Most existing autonomy systems heavily rely on active sensing, e.g., LiDAR, RADAR, and Time-of-Flight sensors, or use (stereo) visible light imaging

Cited by 6SourceScholar
2025

PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain

RA-L 2025

Self-supervised learning is a powerful approach for developing traversability models for off-road navigation, but these models often struggle with inputs unseen during training. Existing methods utilize techniques like evidential deep learning to quantify model uncertainty, helping to identify and a

Cited by 25SourceScholar
2025

Verti-Bench: A General and Scalable Off-Road Mobility Benchmark for Vertically Challenging Terrain

RSS 2025poster

Recent advancement in off-road autonomy has shown promises in deploying autonomous mobile robots in outdoor off-road environments. Encouraging results have been reported from both simulated and real-world experiments. However, unlike evaluating off-road perception tasks on static datasets, benchmark…

Cited by 1PDFcodeScholar
2025

VertiCoder: Self-Supervised Kinodynamic Representation Learning on Vertically Challenging Terrain

ICRA 2025

We present Verticoder, a self-supervised representation learning approach for robot mobility on vertically challenging terrain. Using the same pre-training process, Ver-ticodercan handle four different downstream tasks, in-cluding forward kinodynamics learning, inverse kinodynamics learning, behavio

Cited by 8SourcecodeScholar
2024

CAHSOR: Competence-Aware High-Speed Off-Road Ground Navigation in $\mathbb {SE}(3)$

RA-L 2024

While the workspace of traditional ground vehicles is usually assumed to be in a 2D plane, i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {SE}(2)$</tex-math></inline-formula>, such an assumption may

Cited by 4SourceScholar
2024

Terrain-Attentive Learning for Efficient 6-DoF Kinodynamic Modeling on Vertically Challenging Terrain

IROS 2024poster

Wheeled robots have recently demonstrated superior mechanical capability to traverse vertically challenging terrain (e.g., extremely rugged boulders comparable in size to the vehicles themselves). Negotiating such terrain introduces significant variations of vehicle pose in all six Degrees-of-Freedo…

Cited by 16SourceScholar
2024

Toward Wheeled Mobility on Vertically Challenging Terrain: Platforms, Datasets, and Algorithms

ICRA 2024poster

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-traversable significantly limits wheeled robots’ mobility in real-world, extremely rugged, off-road environments, where part of the t…

Cited by 40SourceScholar
2023

Toward Human-Like Social Robot Navigation: A Large-Scale, Multi-Modal, Social Human Navigation Dataset

IROS 2023poster

Humans are well-adept at navigating public spaces shared with others, where current autonomous mobile robots still struggle: while safely and efficiently reaching their goals, humans communicate their intentions and conform to unwritten social norms on a daily basis; conversely, robots become clumsy…

Cited by 32SourceScholar