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mars
Joined April 2016
Photo by gotcha (@chris3he)Photo by gotcha (@chris3he)Photo by gotcha (@chris3he)Photo by gotcha (@chris3he)Photo by gotcha (@chris3he)Photo by gotcha (@chris3he)Photo by gotcha (@chris3he)Photo by gotcha (@chris3he)Photo by gotcha (@chris3he)
Minimax H3 Max has generates video faster than you can watch it so I hooked it to a twitch livestream! Now you can watch infinite interdimensional cable - link to the stream below
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It's crazy how newer VLA models are up to 2x better on the robodojo benchmark when evaluated in sim, but π0.5 (by now 16 months old) is still leading in real-world evals. It would be great for the field if we could distinguish how much of the difference is down to model quality vs data quantity (& quality). Great work by @MarioChan2002.
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What do you do with 16TB of pure robotic manipulation data? 🤖 You open source it on @huggingface Simple World Lab just dropped HiFi-UMI-2K - an absolutely massive dataset pushing the limits of high-fidelity manipulation. The entire 16TB is already available in the LeRobot format, meaning you can plug this massive scale straight into your pipelines with zero friction. Dataset: huggingface.co/datasets/simp… Paper: huggingface.co/papers/2607.2…
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第一次见中位数数据公布。
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Every single startup selling AI Training Data (July 2026) >50 cos sell data and RL environments to big AI labs and drive AI progress behind the scenes. They total ~$8.5B in rev and ~$100B in valuation, >75% of which are just 4 players: Scale, Surge, Mercor and Handshake.
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New Anthropic research: A global workspace in language models. Of everything happening in your brain right now, only a tiny fraction is consciously accessible—thoughts you can describe, hold in mind, and reason with. We found a strikingly similar divide inside Claude.
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这可能是自动驾驶研发领域「数据工程」最硬核的一次暴力提效。 看到日本 XR 专家 @tokufxug 推荐的开源库 **123D**,我第一反应是:那些还在为了 KITTI、Waymo 等不同数据集格式反复写解析脚本的工程师,真的该看看什么叫「物理级的格式对齐」。 这个库的工程逻辑极其「残暴」: - **全量数据集协议平移**:它将 heterogeneous(异构)的传感器数据,暴力统一到了 Apache Arrow 格式。这意味着无论原始数据是 Protobuf 还是 ROS bag,在你的代码里都变成了标准化的 Columnar(列式)流。 - **Zero-copy 暴力吞吐**:基于 Arrow 的内存映射机制,数据读取几乎没有序列化开销。这种对 I/O 瓶颈的极致压榨,让训练 Foundation Models 的吞吐量直接原地起飞。 - **存储语义对齐**:它采用引用原始路径的策略,避免了 TB 级传感器数据的物理重复存储。这是在用最聪明的架构管理最暴力的数据。 这就是我一直盯着的:**具身智能的门槛,往往不在算法,而在于你处理「物理现实数据」的基建带宽。** 关注 @VedaAI00,同步全球最炸裂的自动驾驶与工程数据实践。👇
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Check out Ariticraft 🦾 - a highly efficient agentic system that generates articulated 3D assets fully automatically at scale! 🚀 articraft3d.github.io/
🚀 Introducing Articraft, a coding agent for articulated 3D asset creation. Articraft writes code, executes it, receives validation feedback, and refines the result into simulation-ready 3D assets with parts, joints, and motion. We’re also releasing Articraft-10K: 10,000+ articulated objects across 250 categories, unlocking large-scale interactive scenes for robotics simulation and physical AI. 🔗 Project page: articraft3d.github.io/ 💻 Code: github.com/mattzh72/articraf…
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I'm curious what happened here..
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Replying to @MarwaEldiwiny
This is where they swapped out human workers controlling it.
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GEN-1 delicately arranges potato chips, and lifts a heavy bag of potatoes — from a gentle touch to a strong grip. Read more about Gen-1 in our blog posts in the comments below ↓
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20年后的 AI 世界,细看极恐啊! 👇
🤖 Made with AI
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我宣布!我拉垮了那么多年的blender技术, 就在今天,将得到飞跃! 因为今天!Claude 宣布推出连接器(Connectors),允许通过对话直接控制 Blender! 还有Autodesk Fusion以及 Adobe Creative Cloud、Ableton、Splice、Affinity、SketchUp 和 Resolume 等软件!
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that is meaningful
We are working to restore mobility that was lost due to disease or spinal cord injury by allowing participants to control robotic arms with their thoughts. See how this is possible.
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An impressive collection of hands
中国ではロボットハンド関連の企業がすでに30社超あると言われており、今年はかなり熱い領域。理由は単純でロボットの量産が進めばハンドの発注総数が増えるだけでなく、現場での実務データ収集用としても使われるからです。 しかもハンドは量産が進むほどコストが下がり、コストが下がるほど導入先が増えるので、この分野は技術競争であると同時に、量産とデータ収集が相互に加速する構造に入っています。 人型ロボットを見るなら、本体だけでなく手の産業化にも注目すると面白いです
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再就业
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gotcha retweeted
This is not a screenshot.
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Teaching robots to perform dexterous manipulation tasks currently requires teleoperation, which limits demonstration quality, speed, and scalability. Instead, why not use human videos? The problem is that a human hand isn’t a robot hand, so data must be retargeted using simulation to resolve issues like collisions and interpenetration when controlling the hand. In VideoManip, @chen_hongyi_ and co-authors built a system to solve this problem, taking in RGB videos of humans performing manipulation tasks and using them to create accurate simulations with which to learn robot policies. Watch episode #73 of RoboPapers, hosted by @micoolcho and @chris_j_paxton, now to learn more!
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The Jensen Huang episode. 0:00:00 – Is Nvidia’s biggest moat its grip on scarce supply chains? 0:16:25 – Will TPUs break Nvidia’s hold on AI compute? 0:41:06 – Why doesn’t Nvidia become a hyperscaler? 0:57:36 – Should we be selling AI chips to China? 1:35:06 – Why doesn’t Nvidia make multiple different chip architectures? Look up Dwarkesh Podcast on YouTube, Apple Podcasts, Spotify, etc. Enjoy!
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