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具身智能

AI 走进物理世界：人形机器人、具身基础模型与真实环境操作能力的进展追踪。

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数据更新至 8月24日 22:17

精选归档 · 第 6 页

第 101–119 条 · 共 119 条

3月9日

星期一 · 1 条

08:00

Hugging Face：Blog（RSS）精选

AI 评分 71/100

LeRobot v0.5.0：扩展每一个维度

Hugging Face 发布了 LeRobot v0.5.0 版本。该版本在数据集、模型、任务和社区四个维度上进行了全面扩展。具体包括：引入了新的现实世界和模拟数据集，推出了支持多任务学习的模型，并扩展了任务范围至移动操作和双臂协调。社区方面，提供了更易用的库、演示应用和教学资源。此次更新的核心目标是降低机器人技术的应用门槛，推动其民主化发展。

Hugging Face 具身智能开源/仓库

推荐理由：开源机器人AI框架大版本更新，降低具身智能研发门槛，值得关注落地进展

3月5日

星期四 · 1 条

22:16

Hugging Face：Blog（RSS）精选

AI 评分 73/100

将机器人AI引入嵌入式平台：数据集记录、VLA微调与设备端优化

NXP在Hugging Face发布技术博客，详细介绍了将视觉语言动作模型应用于嵌入式机器人系统的完整流程。核心工作包括构建真实世界的机器人交互数据集，对VLA模型进行针对性微调，以及实施一系列设备端优化以实现高效部署。该方案旨在解决大型模型在资源受限的嵌入式硬件上运行的挑战，推动机器人AI在边缘端的实际应用。

Hugging Face 具身智能教程/实践端侧

推荐理由：嵌入式AI开发者可掌握从数据集记录到设备端优化的全流程实践。

2月27日

星期五 · 1 条

04:15

Saining Xie@sainingxie精选

世界建模绝非像素渲染：Project Solaris发布多智能体视频世界模型world modeling is never about rendering pixels.rendering is local. world state is global. as soon as more than one agent exists, the only thing that truly matters is the shared representation beneath individual views. that shared representation is what scales into collective capability.this is why I'm super excited to share project Solaris -- our new work focused on building a multiplayer video world model in minecraft.This release includes three main pieces. 1⃣Solaris Engine, a fully featured multiplayer data collection system with built in visuals. the team put a huge amount of work into this since nothing like it really exists yet. https://github.com/solaris-wm/solaris-engine 2⃣Solaris Model, a multiplayer DiT with a new memory efficient self forcing design, trained on 12.6M frames of coordinated Minecraft gameplay. https://github.com/solaris-wm/solaris 3⃣Solaris Eval, which uses a VLM as a judge to evaluate different multiplayer capabilities.read the full technical breakdown by @ojmichel4, and start building with Solaris.https://solaris-wm.github.io/译Project Solaris提出世界建模的本质在于全局共享状态而非局部像素渲染，推出基于Minecraft的多人在线视频世界模型。该系统突破单智能体视角局限，支持任意数量智能体随时介入交互，实现持久化世界状态演化。核心包含三大组件：Solaris Engine多人数据收集系统、基于DiT架构的Solaris Model（采用新型内存高效自强制设计，训练于1260万帧协调游戏数据）、以及使用VLM评判的Solaris Eval评估体系。这一范式转变为构建神经MMORPG服务器奠定基础。

Oscar Michel: 📢Current world models aren't really modeling the world; they're modeling one agent's view of it. Partial observations ≠...

智能体具身智能开源/仓库视频

推荐理由：从单视角到共享全局状态，开源多智能体世界模型或改变AI训练范式

2月26日

星期四 · 1 条

01:22

Jim Fan@DrJimFan精选

22自由度灵巧手人形机器人：从2万小时人类视频学习精细操作We trained a humanoid with 22-DoF dexterous hands to assemble model cars, operate syringes, sort poker cards, fold/roll shirts, all learned primarily from 20,000+ hours of egocentric human video with no robot in the loop.Humans are the most scalable embodiment on the planet. We discovered a near-perfect log-linear scaling law (R² = 0.998) between human video volume and action prediction loss, and this loss directly predicts real-robot success rate.Humanoid robots will be the end game, because they are the practical form factor with minimal embodiment gap from humans. Call it the Bitter Lesson of robot hardware: the kinematic similarity lets us simply retarget human finger motion onto dexterous robot hand joints. No learned embeddings, no fancy transfer algorithms needed. Relative wrist motion + retargeted 22-DoF finger actions serve as a unified action space that carries through from pre-training to robot execution.Our recipe is called "EgoScale":• Pre-train GR00T N1.5 on 20K hours of human video, mid-train with only 4 hours (!) of robot play data with Sharpa hands. 54% gains over training from scratch across 5 highly dexterous tasks. • Most surprising result: a *single* teleop demo is sufficient to learn a never-before-seen task. Our recipe enables extreme data efficiency. • Although we pre-train in 22-DoF hand joint space, the policy transfers to a Unitree G1 with 7-DoF tri-finger hands. 30%+ gains over training on G1 data alone.The scalable path to robot dexterity was never more robots. It was always us.Deep dives in thread:译研究团队提出EgoScale方法，基于20，000小时第一人称人类视频预训练GR00T N1.5，仅用4小时机器人数据即可掌握组装模型车、操作注射器等高灵巧度任务，性能较从头训练提升54%。研究发现人类视频量与动作预测损失呈对数线性缩放关系（R2=0.998）。该方法利用22-DoF手部与人类的运动学相似性，无需复杂迁移算法即可重定向动作。策略可跨硬件迁移至Unitree G1（7-DoF），性能提升30%以上，且仅需单个示教即可学习新任务。

具身智能数据/训练论文/研究

另有 1 家信源报道

推荐理由：人类视频学习呈现完美缩放定律，机器人仅需单演示即可掌握新技能，具身智能迎来数据革命

2月25日

星期三 · 1 条

01:34

Jim Fan@DrJimFan精选

SONIC：半个GPT-1规模的机器人全身控制模型What can half of GPT-1 do? We trained a 42M transformer called SONIC to control the body of a humanoid robot. It takes a remarkable amount of subconscious processing for us humans to squat, turn, crawl, sprint. SONIC captures this "System 1" - the fast, reactive whole-body intelligence - in a single model that translates any motion command into stable, natural motor signals. And it's all open-source!!The key insight: motion tracking is the one, true scalable task for whole body control. Instead of hand-engineering rewards for every new skill, we use dense, frame-by-frame supervision from human mocap data. The data itself encodes the reward function: "configure your limbs in any human-like position while maintaining balance".We scaled humanoid motion RL to an unprecedented scale: 100M+ mocap frames and 500,000+ parallel robots across 128 GPUs. NVIDIA Isaac Lab allows us to accelerate physics at 10,000x faster tick, giving robots many years of virtual experience in only hours of wall clock time. After 3 days of training, the neural net transfers zero-shot to the real G1 robot with no finetuning. 100% success rate across 50 diverse real-world motion sequences.One SONIC policy supports all of the following:• VR whole-body teleoperation • Human video. Just point a webcam to live stream motions. • Text prompts. "Walk sideways", "dance like a monkey", "kick your left foot", etc. • Music audio. The robot dances to the beat, adapting to tempo and rhythm. • VLA foundation models. We plugged in GR00T N1.5 and achieved 95% success on mobile tasks.We open-source the code and model checkpoints!! Deep dive in thread:译SONIC是一个4200万参数的Transformer模型（规模仅半个GPT-1），通过1亿+动作捕捉帧和50万+并行机器人在NVIDIA Isaac Lab中训练，以密集帧级监督替代手工奖励函数。训练3天后零样本迁移至真实G1机器人，在50种动作序列上达100%成功率。单一策略支持VR遥操作、视频动捕、文本指令、音乐响应及VLA模型控制。项目已完全开源。

智能体具身智能开源生态模型发布

推荐理由：42M小模型实现人形机器人全身控制，零样本迁移真实硬件且完全开源，开发者可复现

2月5日

星期四 · 1 条

02:15

Jim Fan@DrJimFan精选

新里程碑：基于世界模型骨干的DreamZero实现零样本开放世界机器人控制New milestone: we trained a robot foundation model on a world model backbone, and enabled zero-shot, open-world prompting capability for new verbs, nouns, and environments. If the world model can "dream" the right future in pixels, then the robot can execute well in motors. We call it "DreamZero", our first World Action Model (WAM).Our team had tons of fun at the lab typing anything we like into an open text prompt, and watch the robot perform tasks it was never trained on. An emergent capability we didn't quite expect. Obviously not GPT-3 reliable yet, but we are marching into the GPT-2 era.Discoveries:• Model and data recipe co-evolve. Compared to VLAs, WAMs learn best from diverse data, breaking away from the conventional wisdom that lots of repeated demos per task are the bread and butter. Diversity >> repetitions.• X-embodiment is extremely hard. Pixels are the answer. Different robot morphologies traditionally have a hard time sharing knowledge well. But if we put video first, pixels become the universal bridge connecting different hardware - even videos of human first-person view. DreamZero shows significant robot2robot and human2robot transfer. With only 55 trajectories on a *new*, unseen hardware (~30 min of teleop), it adapts so quickly and retains zero-shot prompting ability.Yesterday I posted about the "Second Pre-training Paradigm": world models are the next-gen foundation of Physical AI, not language backbones. Today, we are proving it works. And 2026 has just begun.Paper: World Action Models are Zero-Shot Policies.Read it now: (thread)译团队发布DreamZero，首个基于世界模型骨干的World Action Model （WAM）。该模型突破传统Vision-Language-Action范式，通过像素级世界模型实现零样本开放世界提示能力，可执行未训练过的新任务。研究发现WAM依赖多样化数据而非重复演示，并以像素作为跨具身的通用桥梁，实现robot2robot和human2robot知识迁移。仅需55条轨迹（约30分钟遥操作）即可适应全新硬件，验证世界模型作为Physical AI下一代基础的可行性。

智能体 arXiv 具身智能论文/研究

推荐理由：世界模型成为物理AI新底座，机器人零样本泛化能力逼近GPT-2时刻

2月4日

星期三 · 1 条

02:31

Jim Fan@DrJimFan精选

AI 评分 72/100

从"下一个词预测"到"世界建模"：AI预训练的第二范式http://x.com/i/article/2018744045779238912The Second Pre-training ParadigmNext word prediction was the first pre-training paradigm. Now we are living through the second paradigm shift: world modeling, or “next physical state prediction”. Very few understand how far-reaching this shift is, because unfortunately, the most hyped use case of world models right now is AI video slop (and coming up, game slop). I bet with full confidence that 2026 will mark the first year that Large World Models lay real foundations for robotics, and for multimodal AI more broadly.In this context, I define world modeling as predicting the next plausible world state (or a longer duration of states) conditioned on an action. Video generative models are one instantiation of it, where “next states” is a sequence of RGB frames (mostly 8-10 seconds, up to a few minutes) and “action” is a textual description of what to do. Training involves modeling the future changes in billions of hours of video pixels. At the core, video WMs are learnable physics simulators and rendering engines. They capture the counterfactuals, a fancier word for reasoning about how the future would have unfolded differently given an alternative action. WMs fundamentally put vision first.VLMs, in contrast, are fundamentally language-first. From the earliest prototypes (e.g. LLaVA, Liu et al. 2023), the story has mostly been the same: vision enters at the encoder, then gets routed into a language backbone. Over time, encoders improve, architectures get cleaner, vision tries to grow more “native” (as in omni models). Yet it remains a second-class citizen, dwarfed by the muscles the field has spent years building for LLMs. This path is convenient. We know LLMs scale. Our architectural instincts, data recipe design, and benchmark guidance (VQAs) are all highly optimized for language.For physical AI, 2025 was dominated by VLAs: graft a robot motor action decoder on top of a pre-trained VLM checkpoint. It’s really “LVAs”: language > vision > action, in decreasing order of citizenship. Again, this path is convenient, because we are fluent in VLM recipes. Yet most parameters in VLMs are allocated to knowledge (e.g. “this blob of pixels is a Coca Cola brand”), not to physics (“if you tip the coke bottle, it spreads into a brown puddle, stains the white tablecloth, and ruins the electric motor”). VLAs are quite good in knowledge retrieval by design, but head-heavy in the wrong places. The multi-stage grafting design also runs counter to my taste for simplicity and elegance.Biologically, vision dominates our cortical computation. Roughly a third of our cortex is devoted to processing pixels over occipital, temporal, and parietal regions. In contrast, language relies on a relatively compact area. Vision is by far the highest-bandwidth channel linking our brain, our motors, and the physical world. It closes the “sensorimotor loop” — the most important loop to solve for robotics, and requires zero language in the middle.Nature gives us an existential proof of a highly dexterous physical intelligence with minimal language capability. The ape.I’ve seen apes drive golf carts and change brake pads with screwdrivers like human mechanics. Their language understanding is no more than BERT or GPT-1, yet their physical skills are far beyond anything our SOTA robots can do. Apes may not have good LMs, but they surely have a robust mental picture of "what if"s: how the physical world works and reacts to their intervention.The era of world modeling is here. It is bitter lesson-pilled. As Jitendra likes to remind us, the scaling addicts, “Supervision is the opium of the AI researcher.” The whole of YouTube and the rise of smart glasses will capture raw visual streams of our world at a scale far beyond all the texts we ever train on.We shall see a new type of pretraining: next world states could include more than RGBs - 3D spatial motions, proprioception, and tactile sensing are just getting started.We shall see a new type of reasoning: chain of thought in visual space rather than language space. You can solve a physical puzzle by simulating geometry and contact, imagining how pieces move and collide, without ever translating into strings. Language is a bottleneck, a scaffold, not a foundation.We shall face a new Pandora’s box of open questions: even with perfect future simulation, how should motor actions be decoded? Is pixel reconstruction really the best objective, or shall we go into alternative latent spaces? How much robot data do we need, and is scaling teleoperation still the answer? And after all these exercises, are we finally inching towards the GPT-3 moment for robotics?Ilya is right after all. AGI has not converged. We are back to the age of research, and nothing is more thrilling than challenging first principles.译作者指出，AI预训练正经历从"下一个词预测"到"世界建模"的根本性范式转变。世界模型的核心是预测给定行动后的下一个物理状态序列，本质上是可学习的物理模拟器，并将视觉置于首位。相比之下，当前主流的视觉语言模型本质是语言优先，视觉是次要输入。生物智能中视觉处理占据皮层计算的主导地位，是连接大脑、动作与物理世界的高带宽通道。作者以猿类为例，证明强大的物理智能可独立于高级语言存在。他预测，2026年大型世界模型将为机器人技术和多模态AI奠定真正基础，而YouTube等平台的海量视觉数据将远超文本规模，推动这一新范式发展。

具身智能多模态大佬观点

推荐理由：Jim Fan 把世界模型定义为第二次预训练范式转移，核心论点是视觉优先而非语言优先，这个框架对做机器人和多模态的人是真正的路线判断，不是又一篇水文。

1月6日

星期二 · 1 条

06:56

Hugging Face：Blog（RSS）精选

AI 评分 80/100

NVIDIA发布Cosmos Reason 2模型，增强物理AI推理能力

NVIDIA在Hugging Face上发布了Cosmos Reason 2模型，旨在提升物理AI系统的推理能力。该模型通过改进的推理架构，使AI能更准确地理解和预测物理世界的动态与交互，核心升级包括对复杂场景的多步推理、不确定性量化及时间序列数据的深度理解。这一进展将推动机器人、自动驾驶等领域的发展，使AI在现实环境中的决策更可靠、更符合物理规律。

具身智能推理模型发布

推荐理由：物理AI推理能力升级，机器人和具身智能落地的关键拼图