VLA-RAIL: Real-Time Asynchronous Inference for VLA Models in Robotics
Published:Dec 31, 2025 06:59
•1 min read
•ArXiv
Analysis
This paper addresses a critical challenge in deploying Vision-Language-Action (VLA) models in robotics: ensuring smooth, continuous, and high-speed action execution. The asynchronous approach and the proposed Trajectory Smoother and Chunk Fuser are key contributions that directly address the limitations of existing methods, such as jitter and pauses. The focus on real-time performance and improved task success rates makes this work highly relevant for practical applications of VLA models in robotics.
Key Takeaways
- •Introduces VLA-RAIL, a framework for real-time, asynchronous inference in VLA models for robotics.
- •Addresses issues of jitter, stalling, and pauses in robotic action execution.
- •Key components: Trajectory Smoother and Chunk Fuser for smooth transitions.
- •Demonstrates improved performance in simulation and real-world tasks.
- •Aims to be a key infrastructure for large-scale VLA model deployment.
Reference
“VLA-RAIL significantly reduces motion jitter, enhances execution speed, and improves task success rates.”