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

Robotic Vision-Language-Action (VLA) models generalizewell for open-ended manipulation, but their perception is fragile undersensing-stage degradations such as extreme low light, motion blur, andblack clipping. We present E-VLA, an event-augmented VLA frameworkthat improves manipulation robustness when conventional frame-basedvision becomes unreliable. Instead of reconstructing images from events,E-VLA directly leverages motion and structural cues in event streamsto preserve semantic perception and perception-action consistency un-der adverse conditions. We build an open-source teleoperation platformwith a DAVIS346 event camera and collect a real-world synchronizedRGB-event-action manipulation dataset across diverse tasks and illumi-nations. We also propose lightweight, pretrained-compatible event in-tegration strategies and study event windowing for stable deployment.Experiments show that even a simple parameter-free fusion, i.e., over-laying accumulated event maps onto RGB images, could substantiallyimprove robustness in dark and heavy-blur scenes: on Pick-Place at 20lux, success increases from 0% (image-only) to 60% with overlay fusionand to 90% with our event adapter; under severe motion blur (1000 ms-exposure proxy), Pick-Place improves from 0% to 20-25%, and Sortingfrom 5% to 32.5%. Overall, E-VLA provides systematic evidence thatevent-driven perception can be effectively integrated into VLA mod-els, pointing toward robust embodied intelligence beyond conventionalframe-based imaging. Code and dataset will be available at E-VLA.