Zhenyang Chen

OpenAI is hiring for SLAM engineer. Who would say.

我知道你对未来有很多不确定 其实不是 未来已经发生了 只是你尚未察觉 未来10年 人类的寿命会达到150岁 是真的 未来10年 人人都将获得足够的财富与自由 是真的 未来10年 人类的生活将会比之前任何时代都幸福 是真的 再给AI一些时间，一些耐心，一些包容 现在你唯一需要做的就是 好好活着 Do nothing Don't die.

I’m so tired of writing rebuttals to this kind of “lack of novelty” review: “This paper trivially combines A, B, and C, so the algorithmic novelty is limited.” Technically, most (if not all) robotics papers are convex combinations of existing ideas. I still deeply appreciate A+B+C papers—especially when they deliver: - New capabilities: the “trivial combination” unlocks behaviors we simply couldn’t achieve before - Sensible & organic design: A+B+C is clearly the right composition—not some arbitrary A′+B+C′ - Nontrivial interactions: careful analysis of the dynamics, coupling, or failure modes between A, B, C - Rehabilitating old ideas: A was dismissed for years, but paired with modern B/C, it suddenly works—and teaches us why - System-level & "interface" insight: the contribution is not any single piece, but how the pieces talk to each other - Scaling laws or regimes: identifying when/why A+B+C works (and when it doesn’t) - Engineering clarity: making something actually work robustly in the real world is not “trivial” - New problem formulations: sometimes the real novelty is in the reformulation—only under this view does A+B+C make sense. Maybe worth keeping these in mind when reviewing the next A+B+C paper : )

Proud to introduce EgoScale: We pretrained a GR00T VLA model on 20K+ hours of egocentric human video and discovered that robot dexterity can be scaled, not with more robots, but with more human data. A thread on 🧵what we learned. 👇

Can large-scale sim data enable real-world generalization?🤔 In our new work, we introduce a generalizable domain adaptation setting, where policies must handle real-world situations never presented in the real training data. (1/n)