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Main Authors: Chopra, Samarth, McMoil, Alex, Carnovale, Ben, Sokolson, Evan, Kubendran, Rajkumar, Dickerson, Samuel
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2511.05397
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author Chopra, Samarth
McMoil, Alex
Carnovale, Ben
Sokolson, Evan
Kubendran, Rajkumar
Dickerson, Samuel
author_facet Chopra, Samarth
McMoil, Alex
Carnovale, Ben
Sokolson, Evan
Kubendran, Rajkumar
Dickerson, Samuel
contents 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 under $300, capable of modest payloads and workspace. A single unified model jointly outputs discrete and continuous actions, and our adaptive-horizon ensemble monitors motion uncertainty to trigger on-the-fly re-planning 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. Experiment videos and details: https://everydayvla.github.io/
format Preprint
id arxiv_https___arxiv_org_abs_2511_05397
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle EveryDayVLA: A Vision-Language-Action Model for Affordable Robotic Manipulation
Chopra, Samarth
McMoil, Alex
Carnovale, Ben
Sokolson, Evan
Kubendran, Rajkumar
Dickerson, Samuel
Robotics
Computer Vision and Pattern Recognition
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 under $300, capable of modest payloads and workspace. A single unified model jointly outputs discrete and continuous actions, and our adaptive-horizon ensemble monitors motion uncertainty to trigger on-the-fly re-planning 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. Experiment videos and details: https://everydayvla.github.io/
title EveryDayVLA: A Vision-Language-Action Model for Affordable Robotic Manipulation
topic Robotics
Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2511.05397