3 citations · 3 across the 5 of their papers we have counts for
5 papers · 1 filter
Flow-Support-Weighted Proposals for Test-Time Verified VLA Policies
Chen Zhao, Zipeng Ye, Haitao Shen +3
Test-time sampling promises to turn one action prediction into a set of distinct executable candidates that a verifier can choose among. Downstream fine-tuning works against that p…
RoboHarn-Evo: Evolving Hierarchical Physical Knowledge for Self-Improving Robotic Manipulation
Shifeng Bao, Fanding Huang, Yihan Lin +7
Vision-language models can coordinate long-horizon robot manipulation, yet successful task reasoning still depends on whether local physical interactions produce the intended effec…
Training Vision-Language-Action Models with Dense Embodied Chain-of-Thought Supervision
Haoyang Li, Guanlin Li, Youhe Feng +9
Cross-embodiment transfer in vision-language-action (VLA) models remains challenging because low-level state and action spaces differ fundamentally across robot platforms. We obser…
Region-Grounded Report Generation for 3D Medical Imaging: A Fine-Grained Dataset and Graph-Enhanced Framework
Cong Huy Nguyen, Son Dinh Nguyen, Guanlin Li +8
Automated medical report generation for 3D PET/CT imaging is fundamentally challenged by the high-dimensional nature of volumetric data and a critical scarcity of annotated dataset…
TokenPB: Enabling VLA Action Self-Verification via VLM Token Probability Bucketing
Chen Zhao, Zhuoran Wang, Haoyang Li +6
Test-time sampling can improve Vision-Language-Action (VLA) policies, but only if the model can efficiently select a good action from multiple candidates. Prior work often relies o…