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.
Algorithm-Architecture Co-Design for Efficient VLA Inference via Speculative Inference and Verification
arXiv:2608. 15636v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in the field of embodied AI, but their high computational cost and limited predicted action length hinder real-time deployment.
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arXiv AI
Jul 15
Jetson-PI: Towards Onboard Real-Time Robot Control via Foresight-Aligned Asynchronous Inference
arXiv:2607. 12659v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have achieved impressive performance on diverse embodied tasks.
Hugging Face Trending Papers
Jul 29
TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM
Vision-language-action (VLA) models commonly adopt an LLM-centric $V \to L \to A$ pathway, where visual observations are projected into the representation space of a large language model before being decoded into robot actions. Although effective, this design incurs substantial computation and memory overhead at every policy invocation.
arXiv AI
Jul 7
DSWAM: A Dual-System World Action Foundation Model for Fine-Grained Robot Manipulation
arXiv:2607. 04927v1 Announce Type: cross Abstract: World Action Models (WAMs) provide a promising alternative to Vision-Language-Action (VLA) policies by using video-based world modeling as dense supervision for robot action learning.
arXiv AI
Aug 6
Explicit Language Memory for Long-Horizon Planning in Vision-Language-Action Models
arXiv:2608. 04765v1 Announce Type: cross Abstract: Vision-language-action (VLA) models provide a unified paradigm for connecting visual perception, language understanding, and robotic control.
arXiv AI
Jun 9
CT-VAM: A Cerebello-Thalamic-Inspired Vision-Action Model for Efficient Visuomotor Control
arXiv:2606. 09572v1 Announce Type: cross Abstract: Vision-language-action models have shown strong promise for robot manipulation, yet raw language is primarily needed to specify task intent rather than to be repeatedly processed during high-frequency low-level execution.
arXiv Machine Learning
Aug 10
Fast and Accurate: An Adaptive VLA Inference Framework through Environment-aware Model Selection
arXiv:2608. 06434v1 Announce Type: cross Abstract: Embodied intelligence demands both long-horizon reasoning and real-time closed-loop responsiveness.
arXiv AI
Sep 7
RoboSPA: Can VLA Models Go Beyond Simple Scenes and Short-Horizon Tasks?
RoboSPA is a large-scale robotic manipulation dataset and benchmark designed to evaluate Vision‑Language‑Action models on fine‑grained spatial reasoning and long‑horizon procedural planning. It contains 10 task categories, 56 base tasks, and 280 variants across five difficulty levels, with 527K trajectories collected from multiple embodiments and scenes. The benchmark introduces diagnostic metrics beyond binary success, revealing that current VLA models struggle with complex spatial relations, precise execution, and memory‑intensive planning.
arXiv AI
Sep 16
FluxVLA Engine: A One-Stop VLA Engineering Platform for Embodied Intelligence
FluxVLA Engine is an open, configuration‑driven platform that unifies the fragmented components of embodied policy development—datasets, visual‑language and world models, action heads, learning methods, distributed training, simulation evaluation, inference, and robot interfaces—into a reproducible data‑to‑deployment workflow. It adds features such as compositional dual‑arm simulation, scalable automatic data generation, human‑in‑the‑loop rollout and correction, Real‑Time Chunking for fast inference, and lightweight remote GPU serving, thereby linking offline learning, simulation validation, online correction, and real‑robot execution under shared, auditable contracts. The engine aims to eliminate engineering bottlenecks that currently separate promising embodied‑learning algorithms from reliable, reproducible deployment.
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 AI
Aug 17
Reflex: Enabling Fast and Predictive Vision-Language-Action Models for Reaction-Critical Manipulation
arXiv:2608. 14379v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have recently achieved promising performance in robotic manipulation.
arXiv AI
Jul 28
Unified Embodied VLM Reasoning with Robotic Action via Autoregressive Discretized Pre-training
arXiv:2512. 24125v3 Announce Type: replace-cross Abstract: General-purpose robotic systems operating in open-world environments must achieve both broad generalization and high-precision action execution, a combination that remains challenging for existing Vision-Language-Action (VLA) models.