加州大学伯克利分校打造机器人“学徒”：会玩积木、还能组装宜家家具
近日，加州大学伯克利分校 BAIR 实验室的 Sergey Levine 研究团队提出了一种名为 HIL-SERL 的强化学习框架，旨在解决机器人在现实世界中学习复杂操作技能的难题。 这项新技术将人类演示和纠正与高效的强化学习算法相结合，使机器人能够在短短1到2.5小时内掌握各种精密灵巧的操作任务，例如动态操作、精密装配和双臂协作。以前，让机器人学习新技能，那叫一个费劲，就像教一个熊孩子写作业，得手把手地教，还得一遍遍地纠正。更让人头疼的是，现实世界里各种情况复杂多变，机器人往
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