Chenhui Pan
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Verti-Bench: A General and Scalable Off-Road Mobility Benchmark for Vertically Challenging Terrain Open
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 evalu…
Unified Video and Image Representation for Boosted Video Face Forgery Detection Open
Face forgery detection is crucial in preserving the security and integrity of facial data amidst the rapid developments in face manipulation techniques and deep generative models. Existing methods for video face forgery detection typically…
M2P2: A Multi-Modal Passive Perception Dataset for Off-Road Mobility in Extreme Low-Light Conditions Open
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 T…
Traverse the Non-Traversable: Estimating Traversability for Wheeled Mobility on Vertically Challenging Terrain Open
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) regions and then inform subsequent planners to produce tra…
VertiSelector: Automatic Curriculum Learning for Wheeled Mobility on Vertically Challenging Terrain Open
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-and-error learning experiences. However, most RL methods a…
VertiCoder: Self-Supervised Kinodynamic Representation Learning on Vertically Challenging Terrain Open
We present VertiCoder, a self-supervised representation learning approach for robot mobility on vertically challenging terrain. Using the same pre-training process, VertiCoder can handle four different downstream tasks, including forward k…
Reinforcement Learning for Wheeled Mobility on Vertically Challenging Terrain Open
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 achieve smooth collision-free trajectories and at the contr…
PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain Open
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 lear…
Terrain-Attentive Learning for Efficient 6-DoF Kinodynamic Modeling on Vertically Challenging Terrain Open
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 signif…
Learning to Model and Plan for Wheeled Mobility on Vertically Challenging Terrain Open
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 mobility research, showing that wheeled platforms have the pote…
Toward Wheeled Mobility on Vertically Challenging Terrain: Platforms, Datasets, and Algorithms Open
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,…
Predictors for Treatment Outcomes in Patients with Multi-drug Resistant Tuberculosis - China, 2018-2020. Open
The results imply that funds and policy investments should be increased by early monitoring of MDR-TB patients, especially regarding imaging and sputum bacterium. By informing physicians on changes to the therapeutic schedule, treatment ou…