EveryDayVLA: A Vision-Language-Action Model for Affordable Robotic Manipulation

Abstract

While Vision–Language–Action (VLA) models map visual inputs and language instructions directly to robot actions, they often rely on costly hardware and struggle in novel or cluttered scenes. We introduce EverydayVLA, a 6-DOF manipulator that can be assembled for 300, capable of modest payloads and workspaces. A single unified model jointly outputs discrete and continuous actions, and our adaptive-horizon ensembler monitors motion uncertainty to trigger on-the-fly replanning for safe, reliable operation. On LIBERO, EverydayVLA matches state-of-the-art success rates, and in real-world tests it outperforms prior methods by 49% in-distribution and 34.9% out-of-distribution. By combining a state-of-the-art VLA with cost-effective hardware, EverydayVLA democratizes access to a robotic foundation model, and paves the way for economical use in homes and research labs alike.