This work proposes CF-VLA, a coarse-to-fine two-stage formulation that re-structures action generation into a coarse initialization step that constructs an action-aware starting point, followed by a single-step local refinement that corrects residual errors.
IMLE-VLA is introduced, which replaces the iterative action head with a single-step conditional generator trained via conditional Implicit Maximum Likelihood Estimation (cIMLE), and promotes multimodal action coverage, avoiding the mode collapse of naive regression heads while eliminating multi-step sampling entirely.
Kian Hosseinkhani, Qin-He Peng, George Shramko et al.· 1 citation
Conventional flow-based vision-language-action (VLA) models support expressive continuous action generation but rely on multi-step refinement to produce each action chunk, increasing latency in online robot control. To address this issue, we introduce DriftingVLA, a native one-step VLA that generates a complete action...
TEMPO is proposed, a semantic-action decoupled, two-timescale RL post-training framework for VLA models that consistently outperforms both pretrained state-of-the-art VLA models and the RL post-training baseline, while reaching and maintaining higher evaluation rewards on two real-world tasks.
Vision-Language-Action (VLA) models have shown strong generalization in robotic manipulation by combining semantic knowledge from pretrained vision-language models with expressive action-generation policies. Diffusion-based action generators are particularly effective for modeling temporally coherent action chunks, but...
Yi-Heng Ji, Xing-Ru Zhou, Luis Sentis et al.· 0 citations
Vision-Language-Action models are increasingly effective for robotic manipulation, yet most predict actions directly from current observations without explicitly modeling future scene evolution. Recent methods introduce future prediction to improve action generation, but dense future modeling often requires expensive i...
Tian-Fu Li, Hao-Xuan Xu, Wen-Bo Chen et al.· 0 citations
Results indicate that a fixed VLA policy can benefit from conditioning on spatiotemporally aligned demonstrations at test time, and present ICI-VLA, a training and retrieval framework that equips a text-action VLM with few-shot test-time adaptation through in-context demonstrations.
Song-Hua Yang, Zi-Yu Liu, Xue-Tao Li et al.· 0 citations
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