["研究人员从斯坦福大学提出了一种名为 Mobile ALOHA 的低成本整体远程操作系统，用于收集关于整体远程操作的数据。","Mobile ALOHA 通过将其放在轮式底座上，扩展了原始 ALOHA 的功能，使其具有移动能力。","研究人员使用静态 ALOHA 数据集进行模仿学习，通过预训练和联合训练，在移动操作任务中取得了良好的性能。","该研究为日常需要整体远程操作的任务提供了一种低成本、高效的数据收集方法。"]
专注于隐私和安全的网络浏览器
lerobot
动作分块变换器策略，针对AlohaInsertion环境训练，用于精细的双臂操作任务。
动作分块变换器策略，专为AlohaTransferCube环境设计，用于精细的双手机械臂操作任务
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