加州大學伯克利分校打造機器人“學徒”：會玩積木、還能組裝宜家傢俱
近日，加州大學伯克利分校 BAIR 實驗室的 Sergey Levine 研究團隊提出了一種名爲 HIL-SERL 的強化學習框架，旨在解決機器人在現實世界中學習複雜操作技能的難題。 這項新技術將人類演示和糾正與高效的強化學習算法相結合，使機器人能夠在短短1到2.5小時內掌握各種精密靈巧的操作任務，例如動態操作、精密裝配和雙臂協作。以前，讓機器人學習新技能，那叫一個費勁，就像教一個熊孩子寫作業，得手把手地教，還得一遍遍地糾正。更讓人頭疼的是，現實世界裏各種情況複雜多變，機器人往
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