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Announcing Habitat 3.0, simulating humanoid avatars and robots collaborating! - Humanoid sim: diverse skinned avatars - Human-in-the-loop control: mouse/keyboard or VR - Tasks: social navigation and rearrangement Over 1,000 steps per second on 1 GPU for large-scale learning!

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Dhruv Batra

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261,472 Aufrufe • vor 2 Jahren •via X (Twitter)

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home page hero ✨ Design notes: - "Forever" hero text dot pixel FX done in Unicorn Studio. (I will do a whole tutorial on this later. Unicorn's WebGL engine is absolutely wild and very powerful / robust) - built in Framer - I wanted to recreate the iOS unlock effect where your home screen icons cascade into place in a beautifully timed choreography. This took a lot of careful timing using Framer's "Appear" effect on the hero text and surrounding avatars because it was super important that we didn't lose the legibility of our main message ("Build Your Forever Audience") with all the animations. - If you look closely, the choreography is setup to lead your eye through the hero text first starting with "Build Your" then "Forever" and finally "Audience." - With those text layers in place + the surrounding avatars, there is a slight 1 sec pause before the remaining elements slide in below and above (How it works, CTA buttons, announcement badge, and lastly the main nav). - All told the entire loading sequence is 6 seconds - Custom particle system powers the interactive star field (the stars slowly gravitate to your pointer position, and the star field perspective changes ever so subtly as you move your mouse around on the page) - I have 3 shooting stars made of small white line layers that start out off canvas rotated at different angles that shoot across to another point off canvas at random times on a loop effect. - Given this hero scene is in space, I wanted the surrounding avatar elements to "float" in low gravity mode. For this I used Framer's loop effect that slowly oscillates the layer's y position. I then offset the delay of each element randomly to stagger the floating loop so each avatar floats independently/randomly - The final major treatment for this hero scene was the scroll animations on the avatars. I wanted to create a bit of a warp speed effect when you scroll down, as if the avatars were being pulled or sucked into a worm hole as you scroll down below this hero fold. - To accomplish this, I applied Framer's scroll transform affect set to "section in view" on each of the floating avatars, and set the "scroll to" position of the upper avatars to be much, much further away on the y-axis than the "scroll to" position of the lower avatars. (eg. -1700px on upper most avatars vs. -600px on lowest positioned avatars). This effectively causes the upper avatars to slide up off the hero canvas with much greater velocity than the lower positioned avatars. - And when you scroll back up to the hero section, the inverse happens where the lower positioned avatars "arrive back in place" from up above the hero canvas before the upper avatars come back into the scene and settle in place. - Overall I wanted this hero section to feel alive. The floating avatars, particle system with very subtle star movements, and the Caustics effect on the "Forever" text all sort of move at the pace of slow breathing - which is a great pace to create a sense of life and comfort in your scene. Conversion Results (so far) - When this new Framer site launched along with Calaxy v1.9 release on Base a couple weeks ago, we saw a surge in traffic, around 20k page views in the first few days. - Of those 20k hits, 11k visited the app install page ( - which is our main CTA - We saw around 10K new users in the first week after v1.9 launch Overall I'm very happy with the new site and early performance metrics. Lots of tweaking to do but its a good start. If you are a designer building in Framer - hit me with any questions on the above hero notes. Happy to share more specifics! 👾
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home page hero ✨ Design notes: - "Forever" hero text dot pixel FX done in Unicorn Studio. (I will do a whole tutorial on this later. Unicorn's WebGL engine is absolutely wild and very powerful / robust) - built in Framer - I wanted to recreate the iOS unlock effect where your home screen icons cascade into place in a beautifully timed choreography. This took a lot of careful timing using Framer's "Appear" effect on the hero text and surrounding avatars because it was super important that we didn't lose the legibility of our main message ("Build Your Forever Audience") with all the animations. - If you look closely, the choreography is setup to lead your eye through the hero text first starting with "Build Your" then "Forever" and finally "Audience." - With those text layers in place + the surrounding avatars, there is a slight 1 sec pause before the remaining elements slide in below and above (How it works, CTA buttons, announcement badge, and lastly the main nav). - All told the entire loading sequence is 6 seconds - Custom particle system powers the interactive star field (the stars slowly gravitate to your pointer position, and the star field perspective changes ever so subtly as you move your mouse around on the page) - I have 3 shooting stars made of small white line layers that start out off canvas rotated at different angles that shoot across to another point off canvas at random times on a loop effect. - Given this hero scene is in space, I wanted the surrounding avatar elements to "float" in low gravity mode. For this I used Framer's loop effect that slowly oscillates the layer's y position. I then offset the delay of each element randomly to stagger the floating loop so each avatar floats independently/randomly - The final major treatment for this hero scene was the scroll animations on the avatars. I wanted to create a bit of a warp speed effect when you scroll down, as if the avatars were being pulled or sucked into a worm hole as you scroll down below this hero fold. - To accomplish this, I applied Framer's scroll transform affect set to "section in view" on each of the floating avatars, and set the "scroll to" position of the upper avatars to be much, much further away on the y-axis than the "scroll to" position of the lower avatars. (eg. -1700px on upper most avatars vs. -600px on lowest positioned avatars). This effectively causes the upper avatars to slide up off the hero canvas with much greater velocity than the lower positioned avatars. - And when you scroll back up to the hero section, the inverse happens where the lower positioned avatars "arrive back in place" from up above the hero canvas before the upper avatars come back into the scene and settle in place. - Overall I wanted this hero section to feel alive. The floating avatars, particle system with very subtle star movements, and the Caustics effect on the "Forever" text all sort of move at the pace of slow breathing - which is a great pace to create a sense of life and comfort in your scene. Conversion Results (so far) - When this new Framer site launched along with Calaxy v1.9 release on Base a couple weeks ago, we saw a surge in traffic, around 20k page views in the first few days. - Of those 20k hits, 11k visited the app install page ( - which is our main CTA - We saw around 10K new users in the first week after v1.9 launch Overall I'm very happy with the new site and early performance metrics. Lots of tweaking to do but its a good start. If you are a designer building in Framer - hit me with any questions on the above hero notes. Happy to share more specifics! 👾

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🦿Xpeng showed a humanoid robot called IRON whose movement looked so human that the team literally cut it open on stage to prove it is a machine. IRON uses a bionic body with a flexible spine, synthetic muscles, and soft skin so joints and torso can twist smoothly like a person. The system has 82 degrees of freedom in total with 22 in each hand for fine finger control. Compute runs on 3 custom AI chips rated at 2,250 TOPS (Tera Operations Per Second), which is far above typical laptop neural accelerators, so it can handle vision and motion planning on the robot. The AI stack focuses on turning camera input directly into body movement without routing through text, which reduces lag and makes the gait look natural. Xpeng staged the cut-open demo at AI Day in Guangzhou this week, addressing rumors that a performer was inside by exposing internal actuators, wiring, and cooling. Company materials also mention a large physical-world model and a multi-brain control setup for dialogue, perception, and locomotion, hinting at a path from stage demos to service work. Production is targeted for 2026, so near-term tasks will be limited, but the hardware shows a serious step toward human-scale manipulation.

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ENGINEAI just opened registration for URKL, a global humanoid fighting league with an insane ¥10,000,000 (approx. $1.39 million) top prize. 🤖🥊 This is a massive engineering challenge focused on motion control and balance using the "T800" humanoid as the standard bot. The rules are strictly "non-violent," meaning no destructive mods are allowed. You win through better code and smarter protective gear. Here is the breakdown for teams looking to jump in: ➤ Massive Payouts: The winner takes ¥10,000,000 (approx. $1.39 million), second gets ¥2,000,000 (approx. $278,000), and third takes ¥1,000,000 (approx. $139,000). ➤ Hardware Perks: Every team that makes it into the Top 16 officially owns their T800 robot. ➤ Career Fast-Track: Top 8 finalists get a "Green Channel" straight to the final interview for job offers at ENGINEAI. ➤ Registration: Open from March 1 to April 30. Teams need at least 3 members with skills in control, electronics, or mechanical design. ➤ Global Finals: After the qualifiers, the world championship is set for December 2026 through January 2027. Once you are in, the committee hands over the simulation platform and T800 models to start training your boxing algorithms. Full Info: #Robot #Humanoid #Robotics #AI #EmbodiedAI #PhysicalAI #URKL #ENGINEAI #RobotFighting

RoboHub🤖

30,637 Aufrufe • vor 4 Monaten

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This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.

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37,121 Aufrufe • vor 5 Monaten

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Robotics keeps hitting the same wall. Single task RL works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

70,090 Aufrufe • vor 8 Monaten

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Does LLM really need to be a helpful assistant all the time? No. If you want to simulate people, “perfectly helpful” could be the wrong objective. Meet OdysSim, a journey toward LLMs beyond assistants, as behavioral foundation models (10B tokens of real human behavior; 23 sim benchmarks, finally in one place. new open models: outperform or on par with GPT-5.5, Gemini 3.1, or Claude Opus 4.7 in many behavior-sim dimensions). Human behavior simulation is becoming essential. Agent evaluation needs realistic users before real users show up. Medical and classroom training need realistic patients and students. Social science needs synthetic participants at scale. But real people are not ideal assistants. Real patients panic or ignore good advice. Real students misunderstand. Real customers are vague, picky, impatient, or simply leave. Human behavior is messy, diverse, and often imperfect. Frontier LLMs are getting better at math, code, and long-horizon tasks. They are NOT getting better at simulating human behavior. If anything, they drift the other way: more assistant-ish, more homogeneous, fewer of the errors and quirks real humans show. This is no accident. The whole pipeline is built for helpfulness and task success, not behavioral realism. And you can't prompt your way out of that. So we rethink the recipe from scratch and release: 🧠 The OdysSim corpus: 21.4M real human interactions (~10B tokens) from 62 sources, every conversation retrofitted with social grounding (who is talking, and why) 📏 SOUL-Index: 23 human-behavior benchmarks unified into one suite across 5 axes 🤖 OSim-8B: open weights; tops more SOUL-Index benchmarks than any frontier model, acts more like a real user than any of them on τ-bench (nearly matching real humans in the reaction dimension), and writes far more human-like text along the way.

Xuhui Zhou

141,371 Aufrufe • vor 1 Monat

🚀 Introducing EgoExo Forge - built on top of Rerun, Gradio, and Hugging Face hub (I’ll be in San Francisco July 21–29 — if you’re into robotics, egocentric AI, large-scale data collection, or just want to chat, DM me!) In my opinion, large-scale, diverse, and high-quality data is still the largest bottleneck for generalized robotics deployment. I believe that some version of imitation learning from human examples will be the most scalable + clean way to train humanoid robots 🤖 (similar to what Tesla did for Full Self Driving). Teleop is too expensive to collect a large enough dataset in a reasonable manner, so passive collection via egocentric (and in certain cases, exocentric) views feels like the right bet. Over the past few months, I've been trying to build out the scaffolding for this and using Rerun as my underlying infrastructure. Data being collected needs to be easily inspectable + time series and rerun provides the right tooling for this. My goal is to first build out a ground truth representative dataset from already existing open source data, generate some reasonable baselines, and then go out and collect my own data that adheres to the defined schema. 🔍 Starting with open-source datasets 1. EgoDex from Apple 2. HOCap from Nvidia and the University of Texas at Dallas 3. Assembly101 from Meta All these different datasets have different sensor configurations + annotations, so my goal with egoexo-forge is to have one consistent labeling scheme + data layout. I built a data pipeline that aligns all of the different datasets in one general schema assuming the COCO133 keypoint layout that allows for exo+ego, ego only, or exo only Since the scaffolding is already there, it becomes MUCH easier to add other datasets. So the next ones that I'll be including are HD-EPIC kitchens dataset, HOT3D, and finally my own personal iPhone + insta360 go collection method. Once I have a diverse variety of datasets, I'll double down on what I believe to be the key algorithms required to make useful data for imitation learning 📊 1. Camera Pose estimation via SLAM/SFM for ego perspective (and automatic calibration for exo) 2. Human pose estimation for both egocentric + exocentric views 3. Metric 3D reconstruction + object tracking I'll be setting up reasonable open-source baselines for each of these to validate that these datasets work, and then finally try to use the generated datasets for some imitation learning via the pi0-lerobot repo I've been working on. I plan on making a blog post + providing more info on all of this in the near future so stay tuned
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🚀 Introducing EgoExo Forge - built on top of Rerun, Gradio, and Hugging Face hub (I’ll be in San Francisco July 21–29 — if you’re into robotics, egocentric AI, large-scale data collection, or just want to chat, DM me!) In my opinion, large-scale, diverse, and high-quality data is still the largest bottleneck for generalized robotics deployment. I believe that some version of imitation learning from human examples will be the most scalable + clean way to train humanoid robots 🤖 (similar to what Tesla did for Full Self Driving). Teleop is too expensive to collect a large enough dataset in a reasonable manner, so passive collection via egocentric (and in certain cases, exocentric) views feels like the right bet. Over the past few months, I've been trying to build out the scaffolding for this and using Rerun as my underlying infrastructure. Data being collected needs to be easily inspectable + time series and rerun provides the right tooling for this. My goal is to first build out a ground truth representative dataset from already existing open source data, generate some reasonable baselines, and then go out and collect my own data that adheres to the defined schema. 🔍 Starting with open-source datasets 1. EgoDex from Apple 2. HOCap from Nvidia and the University of Texas at Dallas 3. Assembly101 from Meta All these different datasets have different sensor configurations + annotations, so my goal with egoexo-forge is to have one consistent labeling scheme + data layout. I built a data pipeline that aligns all of the different datasets in one general schema assuming the COCO133 keypoint layout that allows for exo+ego, ego only, or exo only Since the scaffolding is already there, it becomes MUCH easier to add other datasets. So the next ones that I'll be including are HD-EPIC kitchens dataset, HOT3D, and finally my own personal iPhone + insta360 go collection method. Once I have a diverse variety of datasets, I'll double down on what I believe to be the key algorithms required to make useful data for imitation learning 📊 1. Camera Pose estimation via SLAM/SFM for ego perspective (and automatic calibration for exo) 2. Human pose estimation for both egocentric + exocentric views 3. Metric 3D reconstruction + object tracking I'll be setting up reasonable open-source baselines for each of these to validate that these datasets work, and then finally try to use the generated datasets for some imitation learning via the pi0-lerobot repo I've been working on. I plan on making a blog post + providing more info on all of this in the near future so stay tuned

Pablo Vela

32,085 Aufrufe • vor 1 Jahr

Imagine having a ping pong robot! 🏓 Researchers and developers building physical AI: meet Reachy 2 from Pollen Robotics, an open-source, humanoid robot for real-world experimentation. It’s a bimanual mobile manipulator: each 7-DOF arm mimics human proportions and can lift up to 3 kg, giving dexterity for object handling. It can be controlled with Python and ROS2 Humble, or go straight into VR teleoperation, use a headset to move Reachy’s arms, hands, and head, and see through its cameras as if you’re in the robot’s own body. Want it to move around? A mobile base with three omnidirectional wheels, rich sensors, and LiDAR lets Reachy 2 navigate and explore its surroundings smoothly. 🗺️ Under the hood, it’s powered by a CPU system that’s ready for machine learning, perfect for loading AI frameworks and testing new models from Hugging Face directly on the robot. Keep making robots more, and more accessible Pollen team! ... and keep making more open source models to make robots more mainstream clem 🤗!
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Imagine having a ping pong robot! 🏓 Researchers and developers building physical AI: meet Reachy 2 from Pollen Robotics, an open-source, humanoid robot for real-world experimentation. It’s a bimanual mobile manipulator: each 7-DOF arm mimics human proportions and can lift up to 3 kg, giving dexterity for object handling. It can be controlled with Python and ROS2 Humble, or go straight into VR teleoperation, use a headset to move Reachy’s arms, hands, and head, and see through its cameras as if you’re in the robot’s own body. Want it to move around? A mobile base with three omnidirectional wheels, rich sensors, and LiDAR lets Reachy 2 navigate and explore its surroundings smoothly. 🗺️ Under the hood, it’s powered by a CPU system that’s ready for machine learning, perfect for loading AI frameworks and testing new models from Hugging Face directly on the robot. Keep making robots more, and more accessible Pollen team! ... and keep making more open source models to make robots more mainstream clem 🤗!

Lukas Ziegler

37,221 Aufrufe • vor 11 Monaten

𝗥𝗼𝗯𝗼𝘁𝘀 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗺𝗼𝗿𝗲 𝗱𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻𝘀. 𝗧𝗵𝗲𝘆 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗳𝗮𝗶𝗹𝘂𝗿𝗲 — 𝗮𝗳𝘁𝗲𝗿 𝘄𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻𝘀. Most robot learning systems assume failure is the end of learning. In our new work, we study whether robots can improve after deployment by learning from their own failures, without any human intervention, teleoperation, or corrective labels. The key idea is simple: human videos contain structure about how the world works. We use them to learn cross-embodiment representations of action, dynamics, and value, enabling a shared predictive space between human behavior and robot experience. This allows a new learning loop: 👉 pretrain on human videos 👉 deploy robot policy 👉 observe failures 👉 reinterpret failures using human priors 👉 improve autonomously We evaluate this across 7 real-world manipulation tasks, showing: 📈 40% → 81% success rate 🏆 Strong improvements over π0.6 RECAP and RISE ✔️ Zero human intervention during post-deployment improvement 🧬 Generalizes across robot embodiments and policy backbones A key finding is that explicit failure repair significantly outperforms failure reweighting, yielding substantially larger gains under identical data conditions (+25 pts vs +5 pts on the same π0.5 base policy). Overall, the results suggest a shift in how we think about robot learning: Human videos are not only for pretraining policies. They can provide the structure needed for continual self-improvement after deployment. 📄 Paper: 🌐 Project: I am grateful for working with the fantastic leads Hanzhi Chen and Anran Zhang, and our collaborators Simon Schaefer, Kejia Chen, Shi Chen, Daniel Cremers. Special thanks to Stefan Leutenegger for co-advising this project with me. ETH Zürich TU München Microsoft Check out Hanzhi's 🧵 for more details
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𝗥𝗼𝗯𝗼𝘁𝘀 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗺𝗼𝗿𝗲 𝗱𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻𝘀. 𝗧𝗵𝗲𝘆 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗳𝗮𝗶𝗹𝘂𝗿𝗲 — 𝗮𝗳𝘁𝗲𝗿 𝘄𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻𝘀. Most robot learning systems assume failure is the end of learning. In our new work, we study whether robots can improve after deployment by learning from their own failures, without any human intervention, teleoperation, or corrective labels. The key idea is simple: human videos contain structure about how the world works. We use them to learn cross-embodiment representations of action, dynamics, and value, enabling a shared predictive space between human behavior and robot experience. This allows a new learning loop: 👉 pretrain on human videos 👉 deploy robot policy 👉 observe failures 👉 reinterpret failures using human priors 👉 improve autonomously We evaluate this across 7 real-world manipulation tasks, showing: 📈 40% → 81% success rate 🏆 Strong improvements over π0.6 RECAP and RISE ✔️ Zero human intervention during post-deployment improvement 🧬 Generalizes across robot embodiments and policy backbones A key finding is that explicit failure repair significantly outperforms failure reweighting, yielding substantially larger gains under identical data conditions (+25 pts vs +5 pts on the same π0.5 base policy). Overall, the results suggest a shift in how we think about robot learning: Human videos are not only for pretraining policies. They can provide the structure needed for continual self-improvement after deployment. 📄 Paper: 🌐 Project: I am grateful for working with the fantastic leads Hanzhi Chen and Anran Zhang, and our collaborators Simon Schaefer, Kejia Chen, Shi Chen, Daniel Cremers. Special thanks to Stefan Leutenegger for co-advising this project with me. ETH Zürich TU München Microsoft Check out Hanzhi's 🧵 for more details

Oier Mees

12,277 Aufrufe • vor 1 Monat

Microsoft presents Windows Agent Arena Evaluating Multi-Modal OS Agents at Scale discuss: Large language models (LLMs) show remarkable potential to act as computer agents, enhancing human productivity and software accessibility in multi-modal tasks that require planning and reasoning. However, measuring agent performance in realistic environments remains a challenge since: (i) most benchmarks are limited to specific modalities or domains (e.g. text-only, web navigation, Q&A, coding) and (ii) full benchmark evaluations are slow (on order of magnitude of days) given the multi-step sequential nature of tasks. To address these challenges, we introduce the Windows Agent Arena: a reproducible, general environment focusing exclusively on the Windows operating system (OS) where agents can operate freely within a real Windows OS and use the same wide range of applications, tools, and web browsers available to human users when solving tasks. We adapt the OSWorld framework (Xie et al., 2024) to create 150+ diverse Windows tasks across representative domains that require agent abilities in planning, screen understanding, and tool usage. Our benchmark is scalable and can be seamlessly parallelized in Azure for a full benchmark evaluation in as little as 20 minutes. To demonstrate Windows Agent Arena's capabilities, we also introduce a new multi-modal agent, Navi. Our agent achieves a success rate of 19.5% in the Windows domain, compared to 74.5% performance of an unassisted human. Navi also demonstrates strong performance on another popular web-based benchmark, Mind2Web. We offer extensive quantitative and qualitative analysis of Navi's performance, and provide insights into the opportunities for future research in agent development and data generation using Windows Agent Arena.
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Microsoft presents Windows Agent Arena Evaluating Multi-Modal OS Agents at Scale discuss: Large language models (LLMs) show remarkable potential to act as computer agents, enhancing human productivity and software accessibility in multi-modal tasks that require planning and reasoning. However, measuring agent performance in realistic environments remains a challenge since: (i) most benchmarks are limited to specific modalities or domains (e.g. text-only, web navigation, Q&A, coding) and (ii) full benchmark evaluations are slow (on order of magnitude of days) given the multi-step sequential nature of tasks. To address these challenges, we introduce the Windows Agent Arena: a reproducible, general environment focusing exclusively on the Windows operating system (OS) where agents can operate freely within a real Windows OS and use the same wide range of applications, tools, and web browsers available to human users when solving tasks. We adapt the OSWorld framework (Xie et al., 2024) to create 150+ diverse Windows tasks across representative domains that require agent abilities in planning, screen understanding, and tool usage. Our benchmark is scalable and can be seamlessly parallelized in Azure for a full benchmark evaluation in as little as 20 minutes. To demonstrate Windows Agent Arena's capabilities, we also introduce a new multi-modal agent, Navi. Our agent achieves a success rate of 19.5% in the Windows domain, compared to 74.5% performance of an unassisted human. Navi also demonstrates strong performance on another popular web-based benchmark, Mind2Web. We offer extensive quantitative and qualitative analysis of Navi's performance, and provide insights into the opportunities for future research in agent development and data generation using Windows Agent Arena.

AK

19,684 Aufrufe • vor 1 Jahr

I believe solving robotics = 90% engineering + 10% research vision. Project GR00T is NVIDIA's moonshot initiative to build physical AGI for humanoid robots. The GEAR Lab is assembling a crack team right now. Join us! Openings: - Sr. Research Engineer, Robotics Systems - Sr. RE, Reinforcement Learning - Sr. RE, Foundation Model Training Infrastructure - Sr. RE, Simulation - Sr. RE, ML Data Pipelines - Research Scientist - Research Intern (both part-time and summer full-time in 2025) For the Sr. positions, we strongly prefer candidates with many years of engineering experience at robotics/autonomous driving companies, or MLOps/large-scale AI teams at big techs. For interns, we welcome ace robotics hackers anywhere! Show me your past works. Job links in the thread. Apply today! Your resumes will be my best Christmas gifts:
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I believe solving robotics = 90% engineering + 10% research vision. Project GR00T is NVIDIA's moonshot initiative to build physical AGI for humanoid robots. The GEAR Lab is assembling a crack team right now. Join us! Openings: - Sr. Research Engineer, Robotics Systems - Sr. RE, Reinforcement Learning - Sr. RE, Foundation Model Training Infrastructure - Sr. RE, Simulation - Sr. RE, ML Data Pipelines - Research Scientist - Research Intern (both part-time and summer full-time in 2025) For the Sr. positions, we strongly prefer candidates with many years of engineering experience at robotics/autonomous driving companies, or MLOps/large-scale AI teams at big techs. For interns, we welcome ace robotics hackers anywhere! Show me your past works. Job links in the thread. Apply today! Your resumes will be my best Christmas gifts:

Jim Fan

103,177 Aufrufe • vor 1 Jahr

Something big is happening in robotics - and it’s hiding in plain sight. This post is not about dancing robots but in the data that powers them. Open robotics datasets have exploded this year, turning the field into a more scalable and collaborative ecosystem. In just two years, Hugging Face datasets grew from 11k to over 600k - and robotics is by far the fastest-growing segment. We went from 1k robotics datasets in 2024 to 27k in 2025! For comparison, text generation, the second-largest category, has only around 5k datasets in 2025. That gap is massive. Open datasets are important because robotics lives and dies by real-world robot data - video, actions, sensors, failures. By making this data easy to upload, reuse, and benchmark, researchers, startups, and large players are now releasing real-robot datasets that would have stayed locked inside labs just a few years ago. Major contributors include NVIDIA, LeRobot initiative, and a rapidly growing maker community. This surge is also enabled by cheaper video storage, better tooling, and an open-source AI culture now spilling into the physical world. And it really matters: open robotics data dramatically lowers entry barriers, accelerates learning-by-doing, and speeds up progress toward generalist and humanoid robots. Robotics won’t scale through hardware alone - but to a large extent through shared data. Viz below from AI World - link to the story and more viz/filters in comment.
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Something big is happening in robotics - and it’s hiding in plain sight. This post is not about dancing robots but in the data that powers them. Open robotics datasets have exploded this year, turning the field into a more scalable and collaborative ecosystem. In just two years, Hugging Face datasets grew from 11k to over 600k - and robotics is by far the fastest-growing segment. We went from 1k robotics datasets in 2024 to 27k in 2025! For comparison, text generation, the second-largest category, has only around 5k datasets in 2025. That gap is massive. Open datasets are important because robotics lives and dies by real-world robot data - video, actions, sensors, failures. By making this data easy to upload, reuse, and benchmark, researchers, startups, and large players are now releasing real-robot datasets that would have stayed locked inside labs just a few years ago. Major contributors include NVIDIA, LeRobot initiative, and a rapidly growing maker community. This surge is also enabled by cheaper video storage, better tooling, and an open-source AI culture now spilling into the physical world. And it really matters: open robotics data dramatically lowers entry barriers, accelerates learning-by-doing, and speeds up progress toward generalist and humanoid robots. Robotics won’t scale through hardware alone - but to a large extent through shared data. Viz below from AI World - link to the story and more viz/filters in comment.

Pierre-Alexandre Balland

186,017 Aufrufe • vor 7 Monaten

Grok Summary of Neuralink’s “Two Years of Telepathy” Update Neuralink’s Telepathy is their pioneering brain-computer interface (BCI) aimed at restoring independence for people with paralysis by translating neural signals into digital commands for controlling devices like computers, phones, and robotic limbs. The article reflects on two years of progress, highlighting clinical trials with 21 participants (Neuralnauts), technical breakthroughs, personal stories of transformation, challenges, and ambitious future goals. • Key Milestones: Enrolled 21 participants worldwide; achieved information transfer rates over 10 bits per second (surpassing able-bodied mouse control); developed a mental ten-finger keyboard for typing up to 40 words per minute; launched the VOICE trial for real-time speech restoration targeting 140 words per minute; expanded trials from three in 2024 to multiple per month in 2025, with no serious device-related adverse events. • Patient Experiences and Impacts: ◦ Noland (first recipient, spinal cord injury): Regained independence for studying, reading, and college (best grades ever); describes it as reclaiming lost life. ◦Nick (paralyzed four years): Controls robotic arm for tasks like feeding; feels natural gestures, restoring a sense of movement. ◦Sebastian (medical student, recent injury): Uses for 17 hours daily to annotate papers and multitask in lectures, boosting productivity. ◦Audrey (20-year injury): Creates abstract art, gains online fame, plans a gallery; feels her mind “a little free.” ◦Jake (ALS): Uses mental keyboard for tasks; sees himself as a “superhero” for his son. ◦Brad (ALS): Controls wheelchair camera to watch his son at events; views ALS as an opportunity for innovation. • Technical Advancements: Translates thoughts into intuitive cursor/robotic control; detects bilateral hand signals from single-sided implants; adapts to individual brain variations during surgery; upcoming upgrades include tripling electrodes to 3000 and exploring less invasive insertion methods. • Challenges: Variations in brain anatomy and disease stages affect performance; early thread retraction issues addressed; communication loss in ALS patients (up to 95% decline ventilation); need for faster, more natural control. • Future Plans: Enhance hardware and procedures for consistency; advance speech restoration; accelerate enrollments; invite quadriplegic individuals (from spinal cord injury, ALS, or stroke) to join the patient registry; seek talent to scale solutions.
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Grok Summary of Neuralink’s “Two Years of Telepathy” Update Neuralink’s Telepathy is their pioneering brain-computer interface (BCI) aimed at restoring independence for people with paralysis by translating neural signals into digital commands for controlling devices like computers, phones, and robotic limbs. The article reflects on two years of progress, highlighting clinical trials with 21 participants (Neuralnauts), technical breakthroughs, personal stories of transformation, challenges, and ambitious future goals. • Key Milestones: Enrolled 21 participants worldwide; achieved information transfer rates over 10 bits per second (surpassing able-bodied mouse control); developed a mental ten-finger keyboard for typing up to 40 words per minute; launched the VOICE trial for real-time speech restoration targeting 140 words per minute; expanded trials from three in 2024 to multiple per month in 2025, with no serious device-related adverse events. • Patient Experiences and Impacts: ◦ Noland (first recipient, spinal cord injury): Regained independence for studying, reading, and college (best grades ever); describes it as reclaiming lost life. ◦Nick (paralyzed four years): Controls robotic arm for tasks like feeding; feels natural gestures, restoring a sense of movement. ◦Sebastian (medical student, recent injury): Uses for 17 hours daily to annotate papers and multitask in lectures, boosting productivity. ◦Audrey (20-year injury): Creates abstract art, gains online fame, plans a gallery; feels her mind “a little free.” ◦Jake (ALS): Uses mental keyboard for tasks; sees himself as a “superhero” for his son. ◦Brad (ALS): Controls wheelchair camera to watch his son at events; views ALS as an opportunity for innovation. • Technical Advancements: Translates thoughts into intuitive cursor/robotic control; detects bilateral hand signals from single-sided implants; adapts to individual brain variations during surgery; upcoming upgrades include tripling electrodes to 3000 and exploring less invasive insertion methods. • Challenges: Variations in brain anatomy and disease stages affect performance; early thread retraction issues addressed; communication loss in ALS patients (up to 95% decline ventilation); need for faster, more natural control. • Future Plans: Enhance hardware and procedures for consistency; advance speech restoration; accelerate enrollments; invite quadriplegic individuals (from spinal cord injury, ALS, or stroke) to join the patient registry; seek talent to scale solutions.

DogeDesigner

17,651 Aufrufe • vor 6 Monaten