3 citations · 4 across the 15 of their papers we have counts for
5 papers · 2 filters
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…