VLA-RAIL: A Real-Time Asynchronous Inference Linker for VLA Models and Robots

TL;DR

VLA-RAIL is a framework that enables real-time, smooth, and high-speed robotic actions by asynchronously fusing vision-language-action model outputs, reducing jitter and improving task success.

Contribution

The paper introduces VLA-RAIL, a novel asynchronous inference framework with trajectory smoothing and chunk fusion for improved robotic VLA model performance.

Findings

Reduces motion jitter in robotic actions

Increases execution speed of robot tasks

Improves success rates in manipulation tasks

Abstract

Vision-Language-Action (VLA) models have achieved remarkable breakthroughs in robotics, with the action chunk playing a dominant role in these advances. Given the real-time and continuous nature of robotic motion control, the strategies for fusing a queue of successive action chunks have a profound impact on the overall performance of VLA models. Existing methods suffer from jitter, stalling, or even pauses in robotic action execution, which not only limits the achievable execution speed but also reduces the overall success rate of task completion. This paper introduces VLA-RAIL (A Real-Time Asynchronous Inference Linker), a novel framework designed to address these issues by conducting model inference and robot motion control asynchronously and guaranteeing smooth, continuous, and high-speed action execution. The core contributions of the paper are two fold: a Trajectory Smoother that effectively filters out the noise and jitter in the trajectory of one action chunk using polynomial fitting and a Chunk Fuser that seamlessly align the current executing trajectory and the newly arrived chunk, ensuring position, velocity, and acceleration continuity between two successive action chunks. We validate the effectiveness of VLA-RAIL on a benchmark of dynamic simulation tasks and several real-world manipulation tasks. Experimental results demonstrate that VLA-RAIL significantly reduces motion jitter, enhances execution speed, and improves task success rates, which will become a key infrastructure for the large-scale deployment of VLA models.