CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies

arXiv:2604.24622 · cs.CV, cs.AI · Submitted 2026-04-27 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-04-27

Updated: 2026-09-04

Code: https://github.com/EmbodiedAI-RoboTron/CF-VLA

Terminology

• Ablation — 13× in this paper · explained in 1 episode
• Alignment — 12× in this paper · explained in 1 episode
• Flow Matching — 10× in this paper · explained in 8 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Core — 5× in this paper · explained in 2 episodes
• Diffusion Model — 4× in this paper · explained in 8 episodes
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Iterative Refinement — 4× in this paper · explained in 3 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Action Flow — 2× in this paper · explained in 1 episode
• Calibration — 2× in this paper · explained in 7 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Normalizing Flow — 2× in this paper · explained in 3 episodes
• Normalizing Flows — 2× in this paper · explained in 4 episodes
• Number of Function Evaluations — 2× in this paper
• World Model — 2× in this paper · explained in 7 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Affordance — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Closed-Loop Control — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Minimal Iterative Policy — 1× in this paper
• VLA Models — 1× in this paper · explained in 3 episodes

Sources

• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
• UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
• Neural Ordinary Differential Equations
• Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
• EveryDayVLA: A Vision-Language-Action Model for Affordable Robotic Manipulation
• Diffusion Trajectory-guided Policy for Long-horizon Robot Manipulation
• Diffusion Transformer Policy
• LaDi-WM: A Latent Diffusion-based World Model for Predictive Manipulation
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• Incorporating Task Progress Knowledge for Subgoal Generation in Robotic Manipulation through Image Edits
• Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
• Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
• OpenVLA: An Open-Source Vision-Language-Action Model
• MolmoAct: Action Reasoning Models that can Reason in Space
• Motion Manifold Flow Primitives for Task-Conditioned Trajectory Generation under Complex Task-Motion Dependencies
• CronusVLA: Towards Efficient and Robust Manipulation via Multi-Frame Vision-Language-Action Modeling
• Flow Matching for Generative Modeling
• LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
• RoboUniView: Visual-Language Model with Unified View Representation for Robotic Manipulation

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