arXiv AI
Jul 28
VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference
arXiv:2512. 01031v2 Announce Type: replace-cross Abstract: Vision-Language-Action models (VLAs) are becoming increasingly capable across diverse robotic tasks.
REACT: Rolling Denoising and Dual Decoupling for Reactive Robot Control with VLA Models
REACT introduces a rolling-denoising framework that enhances the reactivity of flow-based vision-language-action models while preserving long-horizon context. By maintaining a persistent action buffer and denoising the full horizon at each step, REACT refines executed action blocks across multiple observations, reducing reaction latency and improving trajectory smoothness. Dual decoupling further separates sensing, encoding, denoising, and execution to enable high-frequency updates under realistic compute limits.
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arXiv AI
Sep 25
Rolling-WAM: World Action Models with Rolling Imagination
Rolling-WAM is a new formulation for World Action Models that spreads the joint video-action denoising process across multiple replanning cycles. It keeps a sliding window of video-action chunks at different noise levels, fully denoising the immediate chunk for execution while partially refining future chunks. This approach reduces latency, improves closed-loop responsiveness, and achieves a 4.5× speedup in steady-state replanning compared to standard WAMs while maintaining competitive manipulation performance.
arXiv Machine Learning
Sep 17
Reinforcement Learning for Real-Time Vision-Language-Action Policies
The paper presents Real‑Time EXPO‑FT, a reinforcement learning framework that fine‑tunes large Vision‑Language‑Action models for real‑time robotic control. It separates slow, expressive action generation from fast, reactive edits, allowing a lightweight policy to adjust actions based on the latest observation. Experiments on the Kinetix benchmark and four dynamic real‑world tasks show that Real‑Time EXPO‑FT achieves superior performance, improving policy success rates from 42% to 97% with only ten minutes of online data and no human intervention.
arXiv Machine Learning
Jun 9
C$^3$ache: Accelerating World Action Models with Cross Inference Chunk Cache
arXiv:2606. 08962v1 Announce Type: new Abstract: World Action Models (WAMs) generalize better than standard Vision-Language-Action (VLA) policies to novel motions and environments, because a video-modeling objective lets them learn from abundant unlabeled video rather than scarce labeled robot demonstrations.
arXiv Computer Vision
Aug 27
StreamPI: Streaming Multimodal Temporal Modeling for Vision-Language-Action Models
StreamPI introduces a streaming multimodal temporal modeling framework that enhances Vision‑Language‑Action models by adding temporal reasoning without extra parameters. It anchors each visual observation and language instruction pair as a temporal unit, using bidirectional attention for cross‑modal fusion and causal attention for autoregressive streaming inference. The method employs random‑interval streaming training to improve robustness and leverages the LLM backbone’s length extrapolation to inherit pretrained weights, achieving superior performance over pi0.5 on real‑robot and simulation tasks.
Hugging Face Trending Papers
Jul 7
Training-Free Acceleration for Vision-Language-Action Models with Action Caching and Refinement
Vision-Language-Action (VLA) models have emerged as a promising approach for generalizable robotic manipulations. In particular, flow matching-based VLA models have shown remarkable success due to their capability to generate precise and smooth action sequences and capture multimodal distributions.
arXiv Machine Learning
Jul 8
Training-Free Acceleration for Vision-Language-Action Models with Action Caching and Refinement
arXiv:2607. 06370v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising approach for generalizable robotic manipulations.
Hugging Face Trending Papers
Aug 26
StreamPI: Streaming Multimodal Temporal Modeling for Vision-Language-Action Models
Vision-Language-Action (VLA) models have demonstrated effectiveness in robot manipulation, yet state-of-the-art models such as pi0.5 operate under a single-frame paradigm, limiting their ability to re...
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 30
Reactive Real-Time Flow Policies via Asynchronous Distribution Alignment
arXiv:2609.36540v1 Announce Type: cross Abstract: Generalist robot policies such as vision-language-action models (VLAs) have achieved remarkable generalization, but their inference delays can confli...
arXiv Machine Learning
Aug 4
ActionCache: Training-Free Acceleration for Vision-Language-Action Models with Action Caching and Refinement
arXiv:2607. 06370v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising approach for generalizable robotic manipulations.
arXiv AI
Aug 18
EvoScene-VLA: Evolving Scene Beliefs Inside the Action Decoder for Chunked Robot Control
arXiv:2605. 21862v2 Announce Type: replace-cross Abstract: Chunked vision-language-action (VLA) policies predict multi-step robot controls, conditioning each update on the current visual observation alone.