3 citations · 4 across the 15 of their papers we have counts for
15 papers
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…
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…
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…
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…
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…
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…
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…
M2P2: A Multi-Modal Passive Perception Dataset for Off-Road Mobility in Extreme Low-Light Conditions
Aniket Datar, Anuj Pokhrel, Mohammad Nazeri +8
Long-duration, off-road, autonomous missions require robots to continuously perceive their surroundings regardless of the ambient lighting conditions. Most existing autonomy system…
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)…
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…
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…
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…
Terrain-Attentive Learning for Efficient 6-DoF Kinodynamic Modeling on Vertically Challenging Terrain
Aniket Datar, Chenhui Pan, Mohammad Nazeri +2
Wheeled robots have recently demonstrated superior mechanical capability to traverse vertically challenging terrain (e.g., extremely rugged boulders comparable in size to the vehic…
Learning to Model and Plan for Wheeled Mobility on Vertically Challenging Terrain
Aniket Datar, Chenhui Pan, Xuesu Xiao
Most autonomous navigation systems assume wheeled robots are rigid bodies and their 2D planar workspaces can be divided into free spaces and obstacles. However, recent wheeled mobi…
Toward Wheeled Mobility on Vertically Challenging Terrain: Platforms, Datasets, and Algorithms
Aniket Datar, Chenhui Pan, Mohammad Nazeri +1
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 s…