发表机构
Axis Robotics; University of California, Berkeley; Georgia Institute of Technology; Texas A&M University; Johns Hopkins University; University of Pennsylvania; University of Michigan; National University of Singapore; Nanyang Technological University（轴机器人公司; 加州大学伯克利分校; 佐治亚理工学院; 德州农工大学; 约翰·霍普金斯大学; 宾夕法尼亚大学; 密歇根大学; 新加坡国立大学; 南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对机器人操作策略学习中数据管道难扩展的问题，提出AXIS这一可增长社区驱动数据引擎，它能收集、处理数据并组织成任务快照，通过实验表明其能提升模型性能且随数据量增加有一致扩展性。
AI 中文摘要
学习有效的机器人操作策略需要多样、高质量的演示，但现有数据管道因依赖专业硬件、集中式操作员或固定任务套件而难以扩展。我们提出了AXIS，一个用于可扩展机器人学习的可增长社区驱动数据引擎和基准。它支持基于浏览器的远程操作以收集大规模演示，自动生成并验证新操作任务，通过自动成功检查、质量过滤等将社区收集的演示转化为可训练数据。AXIS数据集目前包含207个不同任务和50K+轨迹，还将数据组织成任务快照并通过系统的留出协议评估策略。我们在统一的AXIS评估套件下比较视觉-语言-动作（VLA）策略并分析不同数据量下的扩展行为。在AXIS上持续预训练大幅提高了π0.5的总体成功率，比在RoboCasa365上预训练的模型性能优37.3%，且随着数据量增加呈现一致的扩展性。
英文摘要
Learning effective robot manipulation policies requires diverse, high-quality demonstrations, yet existing data pipelines are often difficult to scale because they rely on specialized hardware, centralized operators, or fixed task suites. We present AXIS, a growable community-driven data engine and benchmark for scalable robot learning, which enables browser-based teleoperation for large-scale demonstration collection, automatically generates and validates new manipulation tasks, and transforms community-collected demonstrations into training-ready data through automated success checking, quality filtering, trajectory smoothing, and visual and physics-based augmentation. The AXIS dataset currently contains 207 diverse tasks and 50K+ trajectories. Meanwhile, AXIS organizes data into task snapshots and evaluates policies with a systematic held-out protocol. We compare vision-language-action (VLA) policies under a unified AXIS evaluation suite and analyze scaling behavior across different data volumes. Continual pretraining on AXIS substantially improves the overall success rate of $π_{0.5}$ by 5.8%, outperforms the model pretrained on RoboCasa365 by 37.3%, and exhibits consistent scaling with increasing data volume, with the largest gains observed under layout, sensor-noise, and camera perturbations.
CommentsProject Website: https://axisaiorg.github.io/AXIS-V1/
