Mobile ALOHA：基于低成本全身遥操作学习双手移动操作
Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation
AI总结：
本文提出Mobile ALOHA低成本全身遥操作系统，通过结合移动底座扩展ALOHA进行数据收集，并利用监督行为克隆与现有静态数据集联合训练，使成功率最高提升90%，实现了复杂的双手移动操作任务。
摘要：
从人类演示中进行模仿学习在机器人领域展现了令人瞩目的性能。然而，大多数结果集中在桌面操作上，缺乏实现通用任务所需的移动性和灵巧性。在本研究中，我们开发了一个用于模仿双手且需要全身控制的移动操作任务的系统。我们首先提出了Mobile ALOHA，这是一个用于数据收集的低成本全身遥操作系统。它通过增加一个移动底座和一个全身遥操作接口来扩展ALOHA系统。使用Mobile ALOHA收集的数据，我们随后进行了监督行为克隆，并发现与现有的静态ALOHA数据集进行联合训练能提升移动操作任务的性能。在每项任务提供50次演示的情况下，联合训练可将成功率最高提升90%，使得Mobile ALOHA能够自主完成复杂的移动操作任务，例如翻炒并盛出一只虾、打开双门壁柜以存放沉重的烹饪锅、呼叫并进入电梯，以及使用厨房水龙头轻轻冲洗用过的平底锅。项目网站：https://mobile-aloha.github.io
英文摘要：
Imitation learning from human demonstrations has shown impressive performance in robotics. However, most results focus on table-top manipulation, lacking the mobility and dexterity necessary for generally useful tasks. In this work, we develop a system for imitating mobile manipulation tasks that are bimanual and require whole-body control. We first present Mobile ALOHA, a low-cost and whole-body teleoperation system for data collection. It augments the ALOHA system with a mobile base, and a whole-body teleoperation interface. Using data collected with Mobile ALOHA, we then perform supervised behavior cloning and find that co-training with existing static ALOHA datasets boosts performance on mobile manipulation tasks. With 50 demonstrations for each task, co-training can increase success rates by up to 90%, allowing Mobile ALOHA to autonomously complete complex mobile manipulation tasks such as sauteing and serving a piece of shrimp, opening a two-door wall cabinet to store heavy cooking pots, calling and entering an elevator, and lightly rinsing a used pan using a kitchen faucet. Project website: https://mobile-aloha.github.io
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
