6 citations · 12 across the 14 of their papers we have counts for
5 papers · 1 filter
How to Better Train VLAs: Lessons Learned From the REAL-I Challenge at ICRA 2026
Jiaming Wang, Jizhuo Chen, Diwen Liu +11
How can robot policies learn more effectively from a fixed demonstration budget? The first Real-world Embodied AI Learning (REAL-I) Challenge at ICRA 2026 examined this question th…
Sampling headroom is not selection gain: a compute-value audit of test-time scaling for video world models
Yuhua Jiang, Junjie Lu, Feifei Gao
Test-time scaling (TTS) can improve generation only when additional compute produces better candidates and the system can reliably identify them. This distinction is especially imp…
SCOPE: Score-Isolated Agentic Optimization for Video World Models
Yuhua Jiang, Jiaming Wang, Qingbin Liu +1
Video world models are increasingly used as simulators for planning and embodied decision making, yet improving them at inference time introduces a subtle evaluation problem: promp…
VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts
Yuhua Jiang, Junjie Lu, Xinyao Qin +4
Vision-language-action (VLA) models inherit rich visual-semantic priors from pre-trained vision-language backbones, but adapting them to robotic control remains challenging. Full f…
CKT-WAM: Parameter-Efficient Context Knowledge Transfer Between World Action Models
Yuhua Jiang, Yijun Guo, Hongbing Yang +7
World action models (WAMs) provide a powerful generative framework for embodied control, yet transferring knowledge across heterogeneous WAMs remains challenging due to mismatched…