移动ALOHA：使用低成本全身远程操作学习双手移动操作

摘要

从人类演示中进行模仿学习在机器人技术中展现出令人印象深刻的性能。然而，大多数结果侧重于桌面操作，缺乏移动性和灵活性，这是执行普遍有用任务所必需的。在这项工作中，我们开发了一个系统，用于模仿双手双臂控制和全身控制的移动操作任务。我们首先介绍了Mobile ALOHA，这是一个用于数据收集的低成本全身远程操作系统。它通过增加移动底座和全身远程操作界面来增强ALOHA系统。利用使用Mobile ALOHA收集的数据，我们随后进行监督行为克隆，并发现与现有静态ALOHA数据集的联合训练可以提高移动操作任务的性能。对于每个任务的50次演示，联合训练可以将成功率提高高达90％，使Mobile ALOHA能够自主完成复杂的移动操作任务，如炒菜和上菜一只虾、打开双门壁橱存放沉重的炊具、呼叫并进入电梯，以及使用厨房水龙头轻轻冲洗使用过的平底锅。项目网站：https://mobile-aloha.github.io

English

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

移动ALOHA：使用低成本全身远程操作学习双手移动操作

Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation

摘要

Support