AsyncVLA adds asynchronous flow matching and a confidence rater to VLA models so they can generate actions on flexible schedules and selectively refine low-confidence tokens before execution.
MoManipVLA: Transferring vision-language- action models for general mobile manipulation
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Per-group action error outperforms total MSE for selecting VLA fine-tuning checkpoints that succeed on real 11-DoF mobile manipulators.
A visibility-aware mobile grasping system with iterative whole-body planning and behavior-tree subgoal generation achieves 68.8% success in unknown static and 58% in dynamic environments, outperforming a baseline by 22.8% and 18%.
The survey frames VLA models as pipelines that generate progressively grounded action tokens and classifies those tokens into eight types to guide future development.
citing papers explorer
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AsyncVLA: Asynchronous Flow Matching for Vision-Language-Action Models
AsyncVLA adds asynchronous flow matching and a confidence rater to VLA models so they can generate actions on flexible schedules and selectively refine low-confidence tokens before execution.
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Per-Group Error, Not Total MSE: Fine-Tuning Vision-Language-Action Models for 11-DoF Mobile Manipulation
Per-group action error outperforms total MSE for selecting VLA fine-tuning checkpoints that succeed on real 11-DoF mobile manipulators.
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Visibility-Aware Mobile Grasping in Dynamic Environments
A visibility-aware mobile grasping system with iterative whole-body planning and behavior-tree subgoal generation achieves 68.8% success in unknown static and 58% in dynamic environments, outperforming a baseline by 22.8% and 18%.
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A Survey on Vision-Language-Action Models: An Action Tokenization Perspective
The survey frames VLA models as pipelines that generate progressively grounded action tokens and classifies those tokens into eight types to guide future development.