arXiv AI
Aug 5
The Geometric Nature and a Free Proxy for Flow-Matching Uncertainty
arXiv:2607. 27933v2 Announce Type: replace Abstract: Flow matching (FM) has become a popular action head paradigm for modern embodied models.
The Geometry of Flow-Matching Uncertainty: A Cost-free Uncertainty Proxy and Its Application in Flow-based VLA Failure Detection
arXiv:2607. 27933v3 Announce Type: replace Abstract: Flow matching (FM) has become a popular action head paradigm for modern embodied models.
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Hugging Face Trending Papers
Jul 30
The Geometric Nature and a Free Proxy for Flow-Matching Uncertainty
Flow matching (FM) has become a popular action head paradigm for modern embodied models. However, as a conditional generative model, it does not explicitly expose its inherent uncertainty, producing faulty action chunks even when it misinterprets the scene or encounters out-of-distribution (OOD) inputs.
arXiv Machine Learning
Jun 17
Uncertainty Quantification for Flow-Based Vision-Language-Action Models
arXiv:2606. 18043v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) combine vision-language backbones with expressive generative action heads trained via flow matching on large-scale robotic datasets.
Hugging Face Trending Papers
Jun 16
Uncertainty Quantification for Flow-Based Vision-Language-Action Models
Vision-language-action models (VLAs) combine vision-language backbones with expressive generative action heads trained via flow matching on large-scale robotic datasets. Despite their strong empirical performance in robotic manipulation, VLAs lack mechanisms to quantify confidence in their predictions and to detect when their actions may be unreliable.
arXiv AI
Sep 18
GeoAAC: Geometry-Based Adaptive Action Chunking from Denoising Trajectories in VLA Policies
GeoAAC introduces a geometry-based adaptive action chunking technique for Vision‑Language‑Action policies, dynamically adjusting the action horizon based on the reliability of current action predictions. By leveraging the geometric variation in Flow Matching denoising trajectories, GeoAAC constructs a horizon‑wise geometric profile that determines the action horizon during a single generation without extra training. Experiments on LIBERO, LIBERO‑Pro, RoboCasa365, and real‑world manipulation tasks demonstrate consistent gains over fixed‑horizon baselines, achieving up to 8.7 percentage points improvement in simulation and raising real‑world success rates from 53.3% to 74.4%.
arXiv Machine Learning
Jun 2
Divergence is Uncertainty: A Closed-Form Posterior Covariance for Flow Matching
arXiv:2605. 00941v4 Announce Type: replace Abstract: Flow matching has become a leading framework for generative modeling, but quantifying the uncertainty of its samples remains an open problem.
arXiv AI
Jul 21
RobustVLA: On Robustness of Vision-Language-Action Model against Multi-Modal Perturbations
arXiv:2510. 00037v5 Announce Type: replace-cross Abstract: In Vision-Language-Actionf(VLA) models, robustness to real-world perturbations is critical for deployment.
arXiv AI
Aug 3
FBFM: A Training-Free Asynchronous Feedback Mechanism for Flow-Matching in World-Action Models Execution
arXiv:2607. 29235v1 Announce Type: cross Abstract: 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 observations--not recursive rollout.
arXiv Machine Learning
Sep 21
$\lambda$-Controlled GRPO: Turning Flow-Matching Ratio Instability into a Budgeted Resource
arXiv:2609. 22041v1 Announce Type: new Abstract: Reinforcement learning is increasingly used to align image generators with reward signals, and Flow-GRPO recently extended this paradigm to flow-matching models by treating the denoising sampler as a stochastic policy that can be optimized from reward feedback.
arXiv Machine Learning
Sep 25
Decoupled Early Exits for Task-Dependent Compute Allocation in Flow-Matching VLAs
The paper introduces a framework for Flow‑Matching Vision‑Language‑Action (VLA) models that allows independent adjustment of backbone depth, action expert depth, and denoising steps. Lightweight Exit Transformers are added at intermediate layers to enable early exits, and a KV Cache synthesis mechanism manages skipped layers so the action expert can exit deeper than the backbone. Experiments on SmolVLA and π0.5 across LIBERO and Meta‑World show that joint tuning of these compute axes reduces latency by 79.2 % and FLOPs by 31.8 %, while improving mean success rate by 5.6 %.
arXiv AI
2d ago
Kinematic MeanFlow: One-Step Action Generation Policy for Robotic Foundation Models
The paper introduces Kinematic MeanFlow (K-MF), a one‑step action generation policy for Robotic Foundation Models that addresses instability in the MeanFlow framework. By decoupling the time derivative into two sub‑interval terms, K-MF captures early and late denoising dynamics separately, reducing error amplification. Experiments show K-MF achieves faster inference—reducing action‑head latency by 67.5%–74.4% and overall end‑to‑end latency by 30.3%–54.9%—while outperforming multi‑step flow matching on various tasks.
arXiv Machine Learning
Jun 5
Flash-WAM: Modality-Aware Distillation for World Action Models
arXiv:2606. 05254v1 Announce Type: new Abstract: World-action models (WAMs) jointly generate future video and robot actions through iterative diffusion, achieving strong performance on manipulation benchmarks but requiring tens of denoising steps, a cost that precludes real-time control.