18 papers
FBFM: A Training-Free Asynchronous Feedback Mechanism for Flow-Matching in World-Action Models Execution
Peize Li, Ruimeng Zhang, Ru Zhang +3
Although world-action models (WAMs) enhance long-horizon robot control by predicting visual evolution before acting, long-horizon reliability demands repeated re-grounding in real…
SIEVE: Structure-Aware Data Selection for Imitation Learning with VLA Models
Changti Wu, Bin Yu, Zhaolong Shen +6
Vision-Language-Action (VLA) models are typically trained by imitation learning on large-scale robot demonstration datasets, but more data does not necessarily yield better policie…
Human-as-Humanoid: Enabling Zero-Shot Humanoid Learning from Ego-Exo Human Videos with Human-Aligned Embodiments
Xiaopeng Lin, Ruoqi Yang, Shijie Lian +14
Vision-language-action (VLA) models across robot embodiments require high-quality observation--action supervision to learn deployable action distributions, yet scaling such robot d…
RoboSemanticBench: Diagnosing Semantic Grounding in Action Prediction for VLA Models
Bin Yu, Yao Zhang, Haishan Liu +9
Vision-language-action (VLA) models are built on the premise that semantic understanding from pretrained language or vision-language backbones should guide robot action prediction.…
LangForce: Bayesian Decomposition of Vision Language Action Models via Latent Action Queries
Shijie Lian, Bin Yu, Xiaopeng Lin +6
Vision-Language-Action (VLA) models have shown promise in robot manipulation but often struggle to generalize to new instructions or complex multi-task scenarios. We identify a cri…
PhysBrain 1.0 Technical Report
Shijie Lian, Bin Yu, Xiaopeng Lin +10
Vision-language-action models have advanced rapidly, but robot trajectories alone provide limited coverage for learning broad physical understanding. PhysBrain 1.0 studies a comple…