4 papers · 1 filter
What Should Robot Policies Learn to Predict? Rethinking Supervision Targets in Vision-Language-Action Learning
Haitao Shen, Hanyu Liu, Yihan Lin +7
Vision-language-action (VLA) policies are typically trained with action prediction as the primary supervision signal, since robots ultimately execute actions. Auxiliary objectives…
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…
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…
From Pixels to Tokens: A Systematic Study of Latent Action Supervision for Vision-Language-Action Models
Yihan Lin, Haoyang Li, Yang Li +4
Latent actions serve as an intermediate representation that enables consistent modeling of vision-language-action (VLA) models across heterogeneous datasets. However, approaches to…