名校博士未必是优秀机器人研究者

“宇宙真的很大，而其中几乎没有任何部分存在于计算机之内。” 当一个难题很难时，就把它做大。@svlevine 把一屋子机器人放进一间改作他用的 Google 会议室，让它们整天抓取物体。 这奏效了。但随后它撞上了一堵墙：经验很多，却没有先验知识。 那堵墙后来变成了 @physical_int。 在 @spc 的这场对话太棒了。我学到了很多！ (00:28) 从电子游戏到机器人技术：Sergey Levine 的历程 (04:24) 创立 Physical Intelligence：为何时机与团队至关重要 (06:30) 深入 Pi Zero：为机器人构建基础模型 (09:37) 从 Pi 0.5 到 Pi 0.7：教机器人推理与泛化 (16:45) 什么造就了优秀的机器人研究者？ (21:10) 解决机器人技术的数据问题并构建学习反馈回路 (28:04) 机器人技术的未来：通用人工智能 vs. 专用机器人 (33:24) 世界模型、数据策略与构建机器人研究文化

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“The universe is really big, and almost none of it is inside computers.” When a problem is hard, make it bigger. @svlevine put a room full of robots in a repurposed Google conference room and had them grasp objects all day. It worked. Then it hit a wall: lots of experience, no prior knowledge. That wall became @physical_int. This conversation at @spc was awesome. I learned a lot! (00:28) From Video Games to Robotics: Sergey Levine's Journey (04:24) Founding Physical Intelligence: Why the Timing and Team Mattered (06:30) Inside Pi Zero: Building a Foundation Model for Robots (09:37) From Pi 0.5 to Pi 0.7: Teaching Robots to Reason and Generalize (16:45) What Makes a Great Robotics Researcher? (21:10) Solving Robotics' Data Problem and Building Learning Feedback Loops (28:04) The Future of Robotics: General-Purpose AI vs. Specialized Robots (33:24) World Models, Data Strategy, and Building a Robotics Research Culture