Martin
@0x_meden • 1,668 subscribers
I won’t wait for life to change, I’ll change it myself | AI & Vibecoding
Videos
A researcher at Jeff Bezos's new AI lab put his entire robot learning course on YouTube: 11 lectures from this spring plus 10 guest talks from Physical Intelligence, NVIDIA, Google DeepMind and Meta, free. "Robot Learning: From Fundamentals to Foundation Models," Oier Mees (Oier Mees), ETH Zürich, Spring 2026. Robot control and MDPs, imitation learning, reinforcement learning, generative models, transformers, world models, generalist robot policies (VLAs), embodied reasoning - 11 lectures and 10 guest talks, about 17 hours, plus 4 coding homeworks on GitHub. One guest talk is Quan Vuong, co-founder of Physical Intelligence, on π0.7, the robot brain they released in April. 13 minutes in, it folds a shirt on a robot arm that had no folding data of its own. Figure is hiring Helix AI Engineers right now, at $200K–$400K. Helix is Figure's VLA, and lecture 9 is how VLAs are built. Lectures: Guest talks: Course: P.S. the other guests: Cheng Chi (Cheng Chi), who built Diffusion Policy, Ted Xiao (Ted Xiao), ex-Google DeepMind, and Lucas Beyer from Meta. my Optimus-style robot brain guide is below if you want the 8-week path.
Martin25,031 次观看 • 20 天前
How to learn Physical Intelligence's robot brain without working at Physical Intelligence: PI was in talks this year to raise $1B at an $11B valuation, and its brain, π0, is open source three of its co-founders teach the ideas inside it on YouTube for $0: Sergey Levine, Chelsea Finn and Quan Vuong here's the order I'd watch them in: 1: copy a human first Levine's Berkeley CS 285, lecture 2 (Spring 2026), is supervised learning of behaviors: a robot copies human demos, then drifts off course after its first small mistake. that drift is why ALOHA and π0 predict a whole chunk of motion at once instead of one step at a time 2: learn the math π0 moves with Stanford AA203, lecture 15 (Spring 2026). an hour in, it explains flow matching, the same math π0 uses to turn a decision into smooth arm motion 3: make it better after the demos run out Chelsea Finn's Stanford CS224R: lecture 16 is RL for real robots, and lecture 17 is a guest lecture from Ashish Kumar, who led AI for Tesla Optimus 4: see the whole brain in one talk Quan Vuong's guest talk in ETH's Robot Learning course is about π0.7. 13 minutes in, it folds a shirt on a robot arm that had no folding data of its own then stop watching and start running it: - openpi, PI's own code and π0 weights, 14.4K stars on GitHub - LeRobot by Hugging Face, 28.5K stars, which fine-tunes π0 on your own demos - a $122 SO-101 arm to record those demos on main insight: the lab in talks at $11 billion is built on three ideas stacked on top of each other: copy a human, move smoothly with flow matching, and let a vision-language model decide what to do next all three are free lectures now. the order is the only part nobody hands you P.S. my Optimus-style robot brain guide below turns this order into an 8-week plan
Martin15,288 次观看 • 18 天前
Stanford charges $70,000 a year to teach robotics. I took their free lectures, put them in the right order and turned them into one website you can use for $0: it takes you from "never wrote a line of Python" to training your own robot policy in 6 months: → 119 free lectures from 7 courses, in the order a Stanford student actually takes them → taught by Chelsea Finn (co-founder of Physical Intelligence), Marco Pavone (NVIDIA), Oussama Khatib and the AI lead of Tesla Optimus → 6 projects in MuJoCo that run on a normal laptop, so you don't need a GPU or a robot to start → a 2-minute self-check that tells you which month to start from if you already code → a progress tracker that remembers every lecture and task you finish → a career map built from 412 real humanoid job posts, and only 2 of them require a PhD I built this for you guys, so if it's useful, reply "robot" and I'll keep updating it every day with new lectures, notebooks and open jobs link in the first reply 👇
Martin11,508 次观看 • 17 天前
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