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20242026
most citedDom, cars don't fly! -- Or do they? In-Air Vehicle Maneuver for High-Speed Off-Road Navigation

1 citations · 1 across the 15 of their papers we have counts for

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cs.RO2026

Verti-WM: A Physics-Aided Exteroceptive World Model for Off-Road Reinforcement Learning

Chenhui Pan, Tong Xu, Xuesu Xiao

Reinforcement learning for off-road navigation requires extensive vehicle-terrain interaction data, which are costly to collect in high-fidelity simulation. World models offer a pr…

cs.RO2026

NeSAM: Neuro-Symbolic Kinodynamics with Soil Adaptation for Off-Road Mobility

Chenhui Pan, Tong Xu, Francesco Cancelliere +1

Accurate prediction of off-road vehicle motion over deformable terrain remains challenging because sinkage, slip, and traction vary with local soil conditions. Existing learning-ba…

cs.RO2026

VertiAKD: Adaptive Off-Road Kinodynamics on Vertically Challenging Terrain

Tong Xu, Chenhui Pan, Francesco Cancelliere +1

Off-road mobility requires autonomous mobile robots to generalize across heterogeneous vehicle fleets and continuously changing terrain conditions. Existing cross-vehicle adaptatio…

cs.RO2026

VertiAdaptor: Online Kinodynamics Adaptation for Vertically Challenging Terrain

Tong Xu, Chenhui Pan, Aniket Datar +1

Autonomous driving in off-road environments presents significant challenges due to the dynamic and unpredictable nature of unstructured terrain. Traditional kinodynamic models ofte…

cs.RO2026

CAR: Cross-Vehicle Kinodynamics Adaptation via Mobility Representation

Tong Xu, Chenhui Pan, Xuesu Xiao

Developing autonomous mobile robot systems typically requires either extensive, platform-specific data collection or relies on simplified abstractions, such as unicycle or bicycle…

cs.RO2025

Adaptive Dynamics Planning for Robot Navigation

Yuanjie Lu, Mingyang Mao, Tong Xu +3

Autonomous robot navigation systems often rely on hierarchical planning, where global planners compute collision-free paths without considering dynamics, and local planners enforce…

cs.RO2025

Verti-Arena: A Controllable and Standardized Indoor Testbed for Multi-Terrain Off-Road Autonomy

Haiyue Chen, Aniket Datar, Tong Xu +6

Off-road navigation is an important capability for mobile robots deployed in environments that are inaccessible or dangerous to humans, such as disaster response or planetary explo…

cs.RO2025★ 1 cited

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

Anuj Pokhrel, Aniket Datar, Xuesu Xiao

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…

cs.RO2025

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

Tong Xu, Chenhui Pan, Madhan B. Rao +4

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…

cs.RO2025

VertiFormer: A Data-Efficient Multi-Task Transformer for Off-Road Robot Mobility

Mohammad Nazeri, Anuj Pokhrel, Alexandyr Card +3

Sophisticated learning architectures, e.g., Transformers, present a unique opportunity for robots to understand complex vehicle-terrain kinodynamic interactions for off-road mobili…

cs.RO2024

Reinforcement Learning for Wheeled Mobility on Vertically Challenging Terrain

Tong Xu, Chenhui Pan, Xuesu Xiao

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

cs.RO2024

Traverse the Non-Traversable: Estimating Traversability for Wheeled Mobility on Vertically Challenging Terrain

Chenhui Pan, Aniket Datar, Anuj Pokhrel +3

Most traversability estimation techniques divide off-road terrain into traversable (e.g., pavement, gravel, and grass) and non-traversable (e.g., boulders, vegetation, and ditches)…

cs.RO2024

VertiSelector: Automatic Curriculum Learning for Wheeled Mobility on Vertically Challenging Terrain

Tong Xu, Chenhui Pan, Xuesu Xiao

Reinforcement Learning (RL) has the potential to enable extreme off-road mobility by circumventing complex kinodynamic modeling, planning, and control by simulated end-to-end trial…

cs.RO2024

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

Mohammad Nazeri, Aniket Datar, Anuj Pokhrel +3

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

cs.RO2024

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

Xiaoyi Cai, James Queeney, Tong Xu +8

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. Ex…