#具身智能

Introducing EgoVerse: an ecosystem for robot learning from egocentric human data. Built and tested by 4 research labs + 3 industry partners, EgoVerse enables both science and scaling 1300+ hrs, 240 scenes, 2000+ tasks, and growing Dataset design, findings, and ecosystem 🧵

🙌 Andrej Karpathy’s lab has received the first DGX Station GB300 -- a Dell Pro Max with GB300. 💚 We can't wait to see what you’ll create @karpathy! 🔗 https://blogs.nvidia.com/blog/gtc-2026-news/#dgx-station @DellTech https://t.co/DHpZA9W7Uz

Advanced Machine Intelligence (AMI) 正在打造新一代 AI 系统，它们能够理解世界、拥有持久记忆、可以推理和规划，并且可控且安全。 我们从相信我们以世界模型为核心的通用智能系统愿景的全球投资者那里筹集了 10.3 亿美元（约 8.9 亿欧元）的一轮融资。本轮融资由 Cathay Innovation、Greycroft、Hiro Capital、HV Capital 和 Bezos Expeditions 共同领投，并有来自世界各地的其他投资者和天使投资人参与。 我们是一支不断壮大的研究者和构建者团队，从第一天起就在巴黎、纽约、蒙特利尔和新加坡运营。 阅读更多：https://amilabs.xyz/ AMI - Real world. Real intelligence.

Advanced Machine Intelligence (AMI) 正在打造新一代 AI 系统，它们能够理解世界、拥有持久记忆、可以推理和规划，并且可控且安全。 我们从相信我们以世界模型为核心的通用智能系统愿景的全球投资者那里筹集了 10.3 亿美元（约 8.9 亿欧元）的一轮融资。本轮融资由 Cathay Innovation、Greycroft、Hiro Capital、HV Capital 和 Bezos Expeditions 共同领投，并有来自世界各地的其他投资者和天使投资人参与。 我们是一支不断壮大的研究者和构建者团队，从第一天起就在巴黎、纽约、蒙特利尔和新加坡运营。 阅读更多：https://amilabs.xyz/ AMI - Real world. Real intelligence.

📢当前的世界模型并没有真正在建模世界；它们建模的是某一个智能体对世界的视角。部分观测 ≠ 世界状态。 未来的世界模型将独立于任何单个智能体的视角。你将能够在任意时间点“放入”任意数量的智能体，而一个持久的世界状态将随着它们的交互而演化。想象一个神经 MMORPG 服务器。🧵[1/10]

We’re excited to introduce the Waymo World Model—a frontier generative mode for large-scale, hyper-realistic autonomous driving simulation built on @GoogleDeepMind’s Genie 3. By simulating the “impossible”, we proactively prepare the Waymo Driver for some of the most rare and complex scenarios—from tornadoes to planes landing on freeways—long before it encounters them in the real world. http://waymo.com/blog/2026/02/the-waymo-world-model-a-new-frontier-for-autonomous-driving-simulation

the divergence of opinion in how robotics plays out is one of the biggest money making (and career making) opportunities in AI

参与无监督 FSD 测试的随行体验 https://t.co/AsyBHnndj8

🤔Visual-spatial reasoning requires a shift from a disembodied, passive paradigm to an embodied, active one: 🤖Grounding V* in humanoid agents! 🚀Introducing H* - a dataset, benchmark, and baseline to enable human-like visual search in real 360° environments! 🧵👇[1/n] https://t.co/SYmKuSjtsA

This is one-shot assembly: you show examples of what to build, and the robot just does it. (see original post: https://generalistai.com/blog) To share more on how this works, the robot is controlled in real time by a neural network that takes in video pixels and outputs 100Hz actions. The video below is part of the raw input passed directly into the model. I also like this view (at 1x speed) because it shows more of the (I think very cool) subtle moments of dexterity near the fingertips 👌 One-shot assembly seemed like a dream even just a year ago — it's not easy. It requires both the high-level reasoning of "what to build" (recognizing the geometry of the structures presented by the human), and the low-level visuomotor control of "how to build it" (purposefully re-orienting individual pieces and nudging them together in place). While possible to manually engineer a complex system for this (e.g. w/ hierarchical control, or explicit state representations), we were curious if our own Foundation model could do it all end-to-end with just some post-training data. Surprisingly, it just worked. Nothing about the recipe is substantially different than any other demo we’ve run in the past, and we’re excited about its implications on model capabilities: • On contextual reasoning, these models can (i) attend to task-related pixels in the peripheral view of the video inputs, and (ii) retain this knowledge in-context while ignoring irrelevant background. This is useful for generalizing to a wide range of real workflows: e.g. paying attention to what’s coming down the conveyor line, or glancing at the instructions displayed on a nearby monitor. • On dexterity, these models can produce contact-rich "commonsense" behaviors that can be difficult to pre-program or write language instructions for e.g. rolling a brick slightly to align its studs against the bottom of another, re-grasping to get a better grip or to move out of the way before a forceful press, or gently pushing the corners of a brick against the mat to rotate it in hand and stand it up vertically (i.e. extrinsic dexterity). These aspects work together to form a capability that resembles fast adaptation — a hallmark of intelligence, relevant for real use cases. This has also expanded my own perspective on what's possible with robot learning, using a recipe that's repeatable for many more skills. This milestone stands on top of the solid technical foundations we’ve built here at Generalist: hardcore controls & hardware, all in-house built models, and a data engine that "just works." We're a small group of hyper-focused engineers, and hands-down the highest talent-density team I’ve ever worked with. We're accelerating and scaling aggressively towards unlocking next-generation robot intelligence. Building Legos is just one example, and it's clear to me that we're headed towards a future where robots can do just about anything we want them to. Its coming, and we're going to make it happen.

The rise of humanoid platforms presents new opportunities and unique challenges. 🤖 Join @yukez at #CoRL2025 as he shares the latest research on robot foundation models and presents new updates with the #NVIDIAIsaac GR00T platform. Learn more 👉http://nvda.ws/4gdfBYY https://t.co/v8GRGPUveA

As a neurosurgeon I care a lot about road safety. By now you’ve probably seen @Waymo’s stunning safety results (like 91% fewer serious crashes). But they didn’t just publish data headlines. They released the raw CSV files and data dictionaries. I did a much deeper analysis. A fascinating story emerges when you analyze how they’re achieving this. This isn’t incremental improvement - it’s categorical. We’re looking at the potential elimination of traffic deaths as a leading cause of mortality. The intersection breakthrough: Waymo has essentially solved intersection crashes, with 95% fewer injury incidents than human drivers in the same locations. That’s transforming the deadliest driving scenario. The national math: If every US vehicle performed like Waymo, we’d prevent 33,000-39,000 deaths annually and save $0.9-1.25 trillion in societal costs. Even partial adoption at 27% would save ~10,000 lives per year. In terms of magnitude, this would be the equivalent of eliminating every pedestrian death nationally in a year. The physics signature: Here’s what fascinates me: 47% of Waymo’s contacts involve less than 1 mph delta-V. They’re not just avoiding crashes; they’re converting unavoidable incidents into gentle bumps. It’s like having physics itself on your side. We’re not talking about marginal safety gains. The data represents a fundamental shift from harm reduction to harm prevention. The methodology matters: I used their dynamic geographic benchmarks (comparing like-for-like road conditions) and verified the findings hold across San Francisco, Phoenix, LA, and Austin. The safety advantage actually increases in more complex urban environments. Link to raw data below…. Notes on my approach: Analysis based on 96 million miles of Waymo Rider-Only (RO) data through June 2025, utilizing Waymo's dynamic geographic benchmarks to compare Waymo Driver performance against human drivers under similar road conditions and operational design domains. The projections for national impact (deaths prevented, societal costs) involve several assumptions. Given Waymo's zero reported fatalities, the direct serious injury reductions were mapped to national fatality statistics using established NHTSA-derived ratios that correlate serious injury crash rates with fatality rates. This extrapolation assumes that Waymo's observed serious injury prevention capability would translate proportionally to fatality prevention. Societal cost savings are estimated by applying average per-fatality and per-injury economic costs (e.g., medical, lost productivity, quality of life) as published by NHTSA, scaling these national averages to the projected number of avoided fatalities and injuries based on Waymo's safety performance. These figures represent the potential annual impact if the Waymo Driver's safety profile were widely integrated into the national fleet. @ethanteicher

(1/N) How close are we to enabling robots to solve the long-horizon, complex tasks that matter in everyday life? 🚨 We are thrilled to invite you to join the 1st BEHAVIOR Challenge @NeurIPS 2025, submission deadline: 11/15. 🏆 Prizes: 🥇 $1,000 🥈 $500 🥉 $300 https://t.co/W63PyzBcMk