论文arXiv 2611.03815今天

AIRoA-0：1.6万小时手持数据训双臂操作，零机器人样本达78%成功率

AIRoA-0系统仅用16151小时开源YUBI手持夹爪演示数据训练，无需任何机器人真机数据。在跨本体双臂操作中平均成功率达78%，显著优于同类VLA模型，并开源数据集、权重及4小时单节点微调方案。

AI 整理

仿真与训练
数据与采集
具身模型

本文由 AI 根据公开内容整理，可能有误，请以原文为准：AIRoA-0: Long-Horizon, Precise, Cross-Embodiment Manipulation from Handheld Data（arXiv 2611.03815）

核心内容

AIRoA-0（A-0）是一套完全基于人类手持演示数据训练的双臂操作系统，全程未采集任何机器人真机数据。研究团队利用开源YUBI手持夹爪录制了16151小时操作视频，涵盖零件分拣、扫地机配件更换、拉链开合及毛巾折叠等任务。在10项跨场景测试中，该系统仅依赖腕部相机即实现78%的平均成功率，远超同数据源下最优VLA模型的27%，且测试环境完全独立于训练数据采集地。

技术上，A-0采用具身无关架构，通过注意力机制自动学习腕部视图关键区域，预测任务阶段并解码连续动作流，无需强化学习或遥操作校正。系统已适配Franka、TX-G2、OpenArm及UR等多种双臂平台。团队开源了含任务/子任务指令的最大规模手持夹爪数据集及预训练权重，支持开发者在8卡H200单节点上4小时内完成新任务后训练，大幅降低具身智能数据获取与迁移门槛。

关键看点

纯手持数据训练：基于16151小时YUBI夹爪演示数据训练，全程零机器人真机数据、零遥操作及零强化学习校正
跨本体泛化性能：在10项未见场景中平均成功率78%，较同数据源最强VLA基线提升51个百分点，仅靠腕部相机感知
开源资产与效率：释放最大规模手持夹爪指令数据集及权重，支持8×H200单节点4小时内完成新任务后训练
硬件兼容生态：验证适配Franka、TX-G2、OpenArm及UR等双臂平台，实现具身无关的连续动作流解码

工程与产业观察

该工作验证了大规模低成本手持数据可替代昂贵的真机遥操数据训练通用操作策略，为具身智能提供了一条去机器人化的数据飞轮路径。其开源YUBI硬件与4小时微调范式，使算法工程师能以消费级成本快速构建垂直场景操作能力，对解决长尾任务数据稀缺具有直接工程参考价值。

英文摘要 (Abstract)

AIRoA-0 (A-0) is a bimanual manipulation system learned from 16,151 hours of handheld demonstrations, with no robot data collected at any stage. The demonstrations are recorded with YUBI, our open-source handheld gripper, which lets people record manipulation data anywhere with no robot present. From a single instruction, A-0 sorts more than a dozen small parts into their numbered cells and changes the mop pads and brush of a robot vacuum; among other tasks, it unzips a pencil case and folds a towel. It performs these tasks using only a camera on each wrist, with no head or scene camera, at a site where none of its training data was recorded. Across the ten tasks of our evaluation, it averages a success rate of 78%, against 27% for the strongest of four other vision-language-action models post-trained on the same task demonstrations. We collect no teleoperation data and use no reinforcement learning or on-robot corrections, and the robot appears only at test time. The system learns which regions of each wrist view matter for the task, predicts the stage the task has reached, and decodes actions as a continuous stream. A-0 is embodiment-agnostic: it runs on robots that mount the YUBI gripper, such as a bimanual Franka, TX-G2, OpenArm and UR. We are releasing the dataset, to our knowledge the largest open dataset of handheld-gripper demonstrations with task and subtask instructions, together with the pre-trained weights and the post-training recipe. Videos of A-0 on every task are on the project page, https://a-0.airoa.io. Anyone can build the gripper, record a task of their own and post-train the model on it in under four hours on a single node of eight H200 GPUs.

作者：Yueh-Hua Wu, Kei Ota, Hanlong Li, Hayata Yuasa, Jason Wang, Kengo Ikeuchi, Kensuke Chiyoma, Makoto Sugiura, Masafumi Takahashi, Masatoshi Tateno, Naoaki Kanazawa, Nicola Scianca, Petr Khrapchenkov, Sa…