ULOHA：用于机器人学习的水下双臂机器人系统
ULOHA: An Underwater Bimanual Robot System for Robot Learning
- The University of Osaka（大阪大学）
- Kobe University（神户大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
ULOHA提出水下双臂机器人学习平台，集成遥操作、多视角感知与策略训练，评估多种模型，实现协调水下双臂行为，并研究跨介质策略迁移。
AI中文摘要：
水下视觉运动策略学习主要集中在单个机械臂上，而双臂模仿学习则主要在空中进行研究。我们提出了ULOHA，一个水下双臂机器人学习平台，该平台将定制设计的领导-跟随硬件与LeRobot的软件扩展相结合，集成了遥操作、多视角感知、演示收集、策略训练和自主部署。真实机器人实验展示了一系列协调的水下双臂行为，包括臂间传递、共享物体操作和浮力驱动的拦截。我们在该平台上评估了ACT、扩散策略和视觉-语言-动作模型SmolVLA。我们研究了为空中操作开发的学习方法和执行策略在水下的表现，考察了气泡干扰、浮力驱动的物体运动、动作执行范围和实时分块。一项单独的单臂研究考察了空气和水之间的策略迁移，并表明在测试条件下，跨越两种介质的演示支持在两种介质中的执行。ULOHA为研究水下环境中耦合的感知和物理效应下的水下双臂机器人学习提供了一个统一的实验平台。附加材料：此https URL
英文摘要：
Underwater visuomotor policy learning has focused primarily on single manipulators, while bimanual imitation learning has been studied largely in air. We present ULOHA, an underwater bimanual robot learning platform that combines custom-designed leader--follower hardware with software extensions to LeRobot, integrating teleoperation, multi-view sensing, demonstration collection, policy training, and autonomous deployment. Real-robot experiments demonstrate a range of coordinated underwater bimanual behaviors, including inter-arm transfer, shared-object manipulation, and buoyancy-driven interception. We evaluate ACT, Diffusion Policy, and the vision--language--action model SmolVLA on the platform. We investigate how learning methods and execution strategies developed for manipulation in air perform underwater, examining bubble disturbances, buoyancy-driven object motion, action-execution horizons, and real-time chunking. A separate single-arm study examines policy transfer between air and water and shows that demonstrations spanning both media support execution in both under the tested conditions. ULOHA provides a unified experimental platform for studying underwater bimanual robot learning under the coupled perceptual and physical effects of underwater environments. Additional material: https://mertcookimg.github.io/uloha/
