E-VLA: Event-Augmented Vision-Language-Action Model for Dark and Blurred Scenes

TL;DR

E-VLA enhances robotic perception in dark and blurry scenes by integrating event streams with vision-language-action models, significantly improving manipulation success rates under adverse conditions.

Contribution

The paper introduces a novel event-augmented VLA framework, new event integration strategies, and a real-world dataset, demonstrating improved robustness in challenging environments.

Findings

Overlay fusion increases success from 0% to 60% in low light.

Event integration improves success from 0% to 20-25% under severe motion blur.

E-VLA demonstrates systematic robustness improvements in real-world manipulation tasks.

Abstract

Robotic Vision-Language-Action (VLA) models generalize well for open-ended manipulation, but their perception is fragile under sensing-stage degradations such as extreme low light, motion blur, and black clipping. We present E-VLA, an event-augmented VLA framework that improves manipulation robustness when conventional frame-based vision becomes unreliable. Instead of reconstructing images from events, E-VLA directly leverages motion and structural cues in event streams to preserve semantic perception and perception-action consistency under adverse conditions. We build an open-source teleoperation platform with a DAVIS346 event camera and collect a real-world synchronized RGB-event-action manipulation dataset across diverse tasks and illumination settings. We also propose lightweight, pretrained-compatible event integration strategies and study event windowing and fusion for stable deployment. Experiments show that even a simple parameter-free fusion, i.e., overlaying accumulated event maps onto RGB images, could substantially improve robustness in dark and blur-heavy scenes: on Pick-Place at 20 lux, success increases from 0% (image-only) to 60% with overlay fusion and to 90% with our event adapter; under severe motion blur (1000 ms exposure), Pick-Place improves from 0% to 20-25%, and Sorting from 5% to 32.5%. Overall, E-VLA provides systematic evidence that event-driven perception can be effectively integrated into VLA models, pointing toward robust embodied intelligence beyond conventional frame-based imaging. Code and dataset will be available at https://github.com/JJayzee/E-VLA.