most citedA Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model

2 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.RO2026

Barrier-Constrained Guidance For Real-time Asynchronous VLA Execution

Wenbo Yu, Jifan Li, Wenke Xia +5

While asynchronous execution enables VLA models to operate in real-time environments by predicting the next chunk during execution, it inevitably introduces boundary misalignment w…

cs.RO2026

Robo-ValueRL: Reliable Value Estimation for Offline-to-Online Reinforcement Learning

Wenke Xia, Pei Ren, Wenbo Yu +10

Offline-to-online reinforcement learning is promising for generalizable robotic manipulation, yet its full-stack complexity obscures reproduction and diagnosis. Within such systems…

cs.RO2026

Affordance2Action: Task-Conditioned Scene-level Affordance Grounding for Real-Time Manipulation

Litao Liu, Yifan Han, Pengfei Yi +9

Task-conditioned manipulation requires grounding instructions to task-relevant functional parts rather than object categories. This setting is scene-dependent and often one-to-many…

cs.RO2026

GeCo-SRT: Geometry-aware Continual Adaptation for Robotic Cross-Task Sim-to-Real Transfer

Wenbo Yu, Wenke Xia, Weitao Zhang +1

Bridging the sim-to-real gap is important for applying low-cost simulation data to real-world robotic systems. However, previous methods are severely limited by treating each trans…

cs.LG2025★ 2 cited

A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model

Wenbo Yu, Anirbit Ghosh, Tobias Sebastian Finn +3

Thanks to recent advances in generative AI, computers can now simulate realistic and complex natural processes. We apply this capability to predict how wildfires spread, a task mad…