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A few weeks ago, we shared our progress on articulated objects and long-horizon tasks. Here are two representative examples: - We've been steadily expanding our asset library to cover more articulated objects. Articulated objects have always been a challenging asset class to handle in simulation. Interacting with them requires... robots to master atomic skills such as pushing, pulling, opening, and closing, and to understand part structure, interaction constraints, and how the object moves. - Long-horizon tasks can now be generated at scale. Long-horizon tasks are the other hard category: they require chaining multiple sub-goals in sequence. A failure early in the task can cascade and make the rest unrecoverable. Axis is scaling along three dimensions at once: data volume, data quality, and task difficulty.show more

Axis Robotics

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13,700 просмотров • 2 месяцев назад •via X (Twitter)

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Today, we publicly released RoboCasa365, a large-scale simulation benchmark for training and systematically evaluating generalist robot models. Built upon our original RoboCasa framework, it offers: • 2,500 realistic kitchen environments; • 365 everyday tasks (basic skills + long-horizon mobile manipulation); • Over 3,200 objects with many articulated fixtures/appliances. All are designed for fully controlled, reproducible benchmarking of robotic policies. Progress in robotic foundation models is real. But it’s still hard to answer basic questions like: How close are we to general-purpose autonomy? What factors drive generalization? What are the model/data scaling curves like? Real-world eval is slow and noisy, and existing sims (like LIBERO, which we built 3 years ago) often lack sufficient task and scene diversity. This benchmark comes with 2,200+ hours of demonstrations and 500K+ trajectories to support studies of multi-task training, pretraining, and continual learning at scale. Check it out at

Today, we publicly released RoboCasa365, a large-scale simulation benchmark for training and systematically evaluating generalist robot models. Built upon our original RoboCasa framework, it offers: • 2,500 realistic kitchen environments; • 365 everyday tasks (basic skills + long-horizon mobile manipulation); • Over 3,200 objects with many articulated fixtures/appliances. All are designed for fully controlled, reproducible benchmarking of robotic policies. Progress in robotic foundation models is real. But it’s still hard to answer basic questions like: How close are we to general-purpose autonomy? What factors drive generalization? What are the model/data scaling curves like? Real-world eval is slow and noisy, and existing sims (like LIBERO, which we built 3 years ago) often lack sufficient task and scene diversity. This benchmark comes with 2,200+ hours of demonstrations and 500K+ trajectories to support studies of multi-task training, pretraining, and continual learning at scale. Check it out at

Yuke Zhu

24,027 просмотров • 5 месяцев назад

How to chain multiple dexterous skills to tackle complex long-horizon manipulation tasks? Imagine retrieving a LEGO block from a pile, rotating it in-hand, and inserting it at the desired location to build a structure. Introducing our new work - Sequential Dexterity 🧵👇

How to chain multiple dexterous skills to tackle complex long-horizon manipulation tasks? Imagine retrieving a LEGO block from a pile, rotating it in-hand, and inserting it at the desired location to build a structure. Introducing our new work - Sequential Dexterity 🧵👇

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159,809 просмотров • 3 лет назад

We are publishing our first deep dive on what we believe is one of the most challenging layers in egocentric data - SLAM and VIO in the context of long-horizon state tracking. We break down how SLAM and VIO fail in egocentric settings - visual features vanish at close range, depth sensors saturate, fast head motion blurs frames, and these failures don't always occur in isolation. They hit at the exact same moment, leading to compounding errors and making the downstream data unusable. We believe the foundation for high-quality egocentric data demands sub-centimeter precision over long episodes ranging from a few minutes to up to an hour.

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We’ve been working with OpenAI for the past few weeks to test their latest Computer-using Agent model. On our evals, CUA has set a new SOTA. Once integrated with an agent, it can complete long-horizon tasks previously impossible. Try CUA on our playground and Act SDK for free!

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I’ve completed all the tasks on Axis Robotics I Made a Video Showing How to Complete Axis Robotics Tasks Easily! Many people are getting stuck while completing Axis Robotics tasks, and some of you asked me to make a detailed video So I made a step-by-step video showing the easiest way to complete the tasks If you haven’t started yet, you can still join early. The project has already raised around $12M, and the Points System is now LIVE 🔗 > Create / log in to your account > Go to Training Tasks > Complete the available tasks > Submit your work properly > Sign your completed tasks > Keep completing different tasks to build your Points Points depend on more than just activity: - Task difficulty - Number of valid tasks - Quality of your submissions - Diversity of tasks - Genuine referrals Don’t keep repeating the same task, Focus on completing different and higher-quality tasks If you’re new to Axis, I recommend starting early and learning the process now instead of waiting until more users join I explained the full process in my video so you can follow it easily Who’s already farming Axis Points?

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Shadat Official | Airdrop

23,952 просмотров • 24 дней назад

Scaling vision-language-action (VLA) models to high-DoF dexterous hands has long been a "holy grail" challenge due to the high-dimensional action space and data scarcity. As a wrap up of the year 2025, we are releasing GR-Dexter, a holistic hardware-model-data framework for generalist manipulation on a bimanual dexterous-hand robot. This is the first VLA system to achieve: ✅ High-DoF Control: Managing a 56-DoF bimanual system (21-DoF per hand). ✅ Long-Horizon Tasks with tool use: Vacuuming, bread serving with tongs, and table decluttering. ✅ Open-World Generalization: Robust performance with unseen objects and abstract instructions. Project page: ArXiv:

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93,908 просмотров • 7 месяцев назад

We’ve raised $50 million in Series A funding from DST Global Partners, NVIDIA (NVIDIA’s venture capital arm), redalpine , prosusventures , and Moonfire Ventures , following our $7.35M seed round from Frst , Moonfire Ventures , and redalpine just a few months earlier, to build the autonomy stack that makes humanoid robots adaptive, intelligent, and ready for real-world deployment at scale. In less than a year, our team has shown that long-horizon whole-body humanoid control can scale across hardware and tasks by leveraging the power of simulation and reinforcement learning. This funding will help us grow our team, scale our compute and robot fleets, and accelerate the commercialization of our autonomy stack with OEM partners globally. You can find more details in the links shared in the comments.

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TBPN

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Google for Developers

39,294 просмотров • 2 месяцев назад

Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

Khurram Javed

52,110 просмотров • 8 месяцев назад

Tesla's Chief Designer Franz on Optimus: "It's going to be huge piece of Tesla, a huge piece in our world, as we look into the future, the ability to take menial tasks and things that you don't necessarily want to do and have them uploaded to somebody, a human, that can do that. We've been designing the robot that really looks at how humans work in the world. This world is designed around humans, and so I think having a robot that can do the things that humans can do can really start to take those menial tasks away and enrich in our lives, make our lives better we get to spend more time doing the things that we love. It's been incredibly fast paced, but the progress is, like, unbelievable in the short amount of time that the team has been working on it, and we've been growing the team and building the project the same time. I think we're breaking ground in this space everyday. The team is really energized and fired out. Elon Musk is incredibly involved in this. It's a great project to be a part of, and seeing where the future is gonna go and how it's going to enrich our lives is super exciting."

Tesla's Chief Designer Franz on Optimus: "It's going to be huge piece of Tesla, a huge piece in our world, as we look into the future, the ability to take menial tasks and things that you don't necessarily want to do and have them uploaded to somebody, a human, that can do that. We've been designing the robot that really looks at how humans work in the world. This world is designed around humans, and so I think having a robot that can do the things that humans can do can really start to take those menial tasks away and enrich in our lives, make our lives better we get to spend more time doing the things that we love. It's been incredibly fast paced, but the progress is, like, unbelievable in the short amount of time that the team has been working on it, and we've been growing the team and building the project the same time. I think we're breaking ground in this space everyday. The team is really energized and fired out. Elon Musk is incredibly involved in this. It's a great project to be a part of, and seeing where the future is gonna go and how it's going to enrich our lives is super exciting."

DogeDesigner

202,945 просмотров • 11 месяцев назад

While we understand how challenging the HOH position can be, Sheba will always stay committed to the task ! Sheba will pull her weight and support every HOH accordingly. The wager is a general team effort and as you know, Queen Sheba doesn’t joke with tasks !!! Let’s stay focused on VOTING SHEBA, we are almost at the end of the line !!! #BBNaija #ShebaGamde

While we understand how challenging the HOH position can be, Sheba will always stay committed to the task ! Sheba will pull her weight and support every HOH accordingly. The wager is a general team effort and as you know, Queen Sheba doesn’t joke with tasks !!! Let’s stay focused on VOTING SHEBA, we are almost at the end of the line !!! #BBNaija #ShebaGamde

Sheba BBNAIJA

69,498 просмотров • 15 дней назад

How well do today’s frontier models handle long-horizon, multi-step web agent tasks, such as identifying the top 25 U.S. CS PhD programs with ML/AI faculty likely accepting students and compiling the results into a structured sheet? Check out our new work on Odysseys: Benchmarking Web Agents on Realistic Long Horizon Tasks Paper: Leaderboard: We introduce Odysseys, a benchmark of 200 long-horizon tasks derived from real browsing sessions and evaluated on the live Internet. We show that binary pass/fail is inadequate in this setting and propose rubric-based evaluation, which better aligns with human judgment and provides more informative signals. Across leading models, the best achieves only 44.5% success, leaving substantial headroom. We further introduce a Trajectory Efficiency metric (rubric score per step) and find efficiency remains extremely low (1.15%), highlighting a key bottleneck. Odysseys provides a realistic benchmark for measuring progress toward web agents capable of sustained, efficient, real-world operation. See a more detailed thread by Jing Yu Koh.

How well do today’s frontier models handle long-horizon, multi-step web agent tasks, such as identifying the top 25 U.S. CS PhD programs with ML/AI faculty likely accepting students and compiling the results into a structured sheet? Check out our new work on Odysseys: Benchmarking Web Agents on Realistic Long Horizon Tasks Paper: Leaderboard: We introduce Odysseys, a benchmark of 200 long-horizon tasks derived from real browsing sessions and evaluated on the live Internet. We show that binary pass/fail is inadequate in this setting and propose rubric-based evaluation, which better aligns with human judgment and provides more informative signals. Across leading models, the best achieves only 44.5% success, leaving substantial headroom. We further introduce a Trajectory Efficiency metric (rubric score per step) and find efficiency remains extremely low (1.15%), highlighting a key bottleneck. Odysseys provides a realistic benchmark for measuring progress toward web agents capable of sustained, efficient, real-world operation. See a more detailed thread by Jing Yu Koh.

Russ Salakhutdinov

22,518 просмотров • 4 месяцев назад

Thrilled to share one of my favorite works this year: DexNDM! We bridge the Sim2Real gap for dexterous in-hand rotation, achieving a true "0-to-1" advancement. The key? DexNDM learns from biased, real-world data without needing any successful demonstrations. Now a general-purpose dexterous hand can stably rotate large books, long rods, & complex objects around any axis, from any wrist pose. This powerful primitive enables complex, long-horizon tasks like teleoperated screwing and furniture assembly. 📄 Paper: 🌐 Project Page:

Thrilled to share one of my favorite works this year: DexNDM! We bridge the Sim2Real gap for dexterous in-hand rotation, achieving a true "0-to-1" advancement. The key? DexNDM learns from biased, real-world data without needing any successful demonstrations. Now a general-purpose dexterous hand can stably rotate large books, long rods, & complex objects around any axis, from any wrist pose. This powerful primitive enables complex, long-horizon tasks like teleoperated screwing and furniture assembly. 📄 Paper: 🌐 Project Page:

Li Yi

38,751 просмотров • 9 месяцев назад

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Synthetic data will provide the next trillion tokens to fuel our hungry models. I'm excited to announce MimicGen: massively scaling up data pipeline for robot learning! We multiply high-quality human data in simulation with digital twins. Using 50,000 training episodes across 18 tasks, multiple simulators, and even in the real-world! The idea is simple: 1. Humans tele-operate the robot to complete a task. It is extremely high-quality but also very slow and expensive. 2. We create a digital twin of the robot and the scene in high-fidelity, GPU-accelerated simulation. 3. We can now move objects around, replace with new assets, and even change the robot hand - basically augment the training data with procedural generation. 4. Export the successful episodes, and feed that to a neural network! You now have an near-infinite stream of data. One of the key reasons that robotics lags far behind other AI fields is the lack of data: you cannot scrape control signals from the internet. They simply don't exist in-the-wild. MimicGen shows the power of synthetic data and simulation to keep our scaling laws alive. I believe this principle apply beyond robotics. We are quickly exhausting the high-quality, real tokens from the web. Artificial intelligence from artificial data will be the way forward. We are big fans of the OSS community. As usual, we open-source everything, including the generated dataset! - Website: - Paper: - Dataset is hosted on HuggingFace (thanks AK!!): - Code: MimicGen is led by Ajay Mandlekar, deep dive in the thread:

Jim Fan

332,238 просмотров • 2 лет назад

Axis Robotics Point System - Free Airdrop Guide: Axis Robotics {Robotics Sector} Currently, I think this is the one of the free airdrop you should focus on because: - Raised $12M (led by Hack VC) - Building on AI + Robotics (robotics could be the next hot sector after AI) - Cost free tasks (only requires effort) - Low competition due to the difficult tasks + negative sentiment around airdrops STEPS 👇🏻 🔗 • Create an account • Complete all the available tasks. There are many live tasks to complete. It might feel hard when you start but once you adapt, it gets easier. • The more tasks you complete, the more points you'll earn • Sign all the tasks you complete (you can use bulk signing) Point distribution: Based on: • Volume of tasks • Difficulty level • Variety of tasks completed Points are distributed every 2 weeks (MONDAY) Snapshot taken on Friday. Special tasks: There are some special tasks that give higher points, but not everyone can access them (requires an access code). Will update if I get any. For special tasks, click on Alliance and complete the tasks there. Still very early, as the point system went live only a few days ago. You can contribute now and try to climb the leaderboard. Added a video showing how the tasks are completed by controlling the robots👇🏻

Axis Robotics Point System - Free Airdrop Guide: Axis Robotics {Robotics Sector} Currently, I think this is the one of the free airdrop you should focus on because: - Raised $12M (led by Hack VC) - Building on AI + Robotics (robotics could be the next hot sector after AI) - Cost free tasks (only requires effort) - Low competition due to the difficult tasks + negative sentiment around airdrops STEPS 👇🏻 🔗 • Create an account • Complete all the available tasks. There are many live tasks to complete. It might feel hard when you start but once you adapt, it gets easier. • The more tasks you complete, the more points you'll earn • Sign all the tasks you complete (you can use bulk signing) Point distribution: Based on: • Volume of tasks • Difficulty level • Variety of tasks completed Points are distributed every 2 weeks (MONDAY) Snapshot taken on Friday. Special tasks: There are some special tasks that give higher points, but not everyone can access them (requires an access code). Will update if I get any. For special tasks, click on Alliance and complete the tasks there. Still very early, as the point system went live only a few days ago. You can contribute now and try to climb the leaderboard. Added a video showing how the tasks are completed by controlling the robots👇🏻

Abhi.eth

20,394 просмотров • 19 дней назад

Progress in open models is keeping Big AI labs up at night, and I'm here for it! We have a brand new open-weight multimodal model optimized for long-horizon tasks. This model is really good at something: it can work on tasks that keep evolving over time. • 280B total parameters, but only 16B active • 512K context window • Understands text, images, and audio • Strong reasoning, coding, and tool use But the best of all: the model learns and adapts to new information! Imagine you start running an agent today to solve a problem, and while it's working, you get new information that changes the initial conditions, or you change your mind. The agents you run today don't have issues with short tasks and goals that don't change, but reality is messy, and that makes it hard for long-horizon agents to succeed. The new dots3-note Preview model introduces TEMPO. TEMPO is a new reinforcement learning technique that lets the model periodically pause and critique its own progress. Basically, from time to time, the agent asks itself: "Am I getting closer to the goal, or am I wasting my time?" The same model switches between actor and critic. The actor works on the problem. The critic looks at the current state, reasons about how much progress it has made, and determines what should happen next. TEMPO gives the model feedback along the way. This is huge for any agent that can work on long-horizon tasks without wasting its time.

Santiago

79,544 просмотров • 9 дней назад

Robots need to be able to apply pressure and make contact with objects as needed in order to accomplish their tasks. From compliance to working safely around humans to whole-body manipulation of heavy objects, combining force and position control can dramatically expand the capabilities of robots. This is especially true for legged robots, which have so much ability to exert forces on the world around them. But how do we train robots which can do this? Baoxiong Jia tells us more in our discussion of his team’s recent, Best Paper Award winning work on learning a unified policy for position and force control, called UniFP. To learn more, watch Episode #49 of RoboPapers, hosted by Michael Cho - Rbt/Acc and Chris Paxton.

Robots need to be able to apply pressure and make contact with objects as needed in order to accomplish their tasks. From compliance to working safely around humans to whole-body manipulation of heavy objects, combining force and position control can dramatically expand the capabilities of robots. This is especially true for legged robots, which have so much ability to exert forces on the world around them. But how do we train robots which can do this? Baoxiong Jia tells us more in our discussion of his team’s recent, Best Paper Award winning work on learning a unified policy for position and force control, called UniFP. To learn more, watch Episode #49 of RoboPapers, hosted by Michael Cho - Rbt/Acc and Chris Paxton.

RoboPapers

44,813 просмотров • 8 месяцев назад