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…
Learning Scene-Aware Humanoid Locomotion through 3D Clutter from Immersive Human Demonstrations
Beichen Wang, Tong Xu, Daniel Kosukhin +3
While learning from human motions has enabled highly dynamic humanoid skills such as dancing and martial arts in obstacle-free space, traversal through densely cluttered environmen…
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…
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…
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…
CARoL: Context-aware Adaptation for Robot Learning
Zechen Hu, Tong Xu, Xuesu Xiao +1
Using Reinforcement Learning (RL) to learn new robotic tasks from scratch is often inefficient. Leveraging prior knowledge has the potential to significantly enhance learning effic…
Reward Training Wheels: Adaptive Auxiliary Rewards for Robotics Reinforcement Learning
Linji Wang, Tong Xu, Yuanjie Lu +1
Robotics Reinforcement Learning (RL) often relies on carefully engineered auxiliary rewards to supplement sparse primary learning objectives to compensate for the lack of large-sca…
Decremental Dynamics Planning for Robot Navigation
Yuanjie Lu, Tong Xu, Linji Wang +2
Most, if not all, robot navigation systems employ a decomposed planning framework that includes global and local planning. To trade-off onboard computation and plan quality, curren…
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…
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…
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…