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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

61,779 subscribers

14,217 views • 3 months ago •via X (Twitter)

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35 Comments

Little Banana(✱,✱)3 months ago

Hopefully the hitboxes for some objects and the hitbox for the two handed gripper will be fixed soon👀 so excited to do more tasks with a more refined and seamless experience to provide the best data for the team as task scale

Base Hub 🛡️3 months ago

👀

captain.base.eth3 months ago

Look good

Dr.Nheo3 months ago

Sounds like solid progress, curious how you're handling the complexity of those joints.

Zayn Shelby3 months ago

các nhiệm vụ khó hơn đã sắp cập bến, và chúng ta cần phải nâng cấp khả năng làm nhiệm vụ của mình nhanh và chính xác hơn

Titli🧚3 months ago

Superb knowledge team

MrChuoi2793 months ago

LFG

MD. Khalid3 months ago

Keep building

Arc Hub3 months ago

looks cool

RahulKumar3 months ago

Keep building team We are with you

harty (✱,✱)3 months ago

Impressive progress. Looking forward to what's next.

_W☆яяιoя3 months ago

Great update, thanks 🙏

Blackco3 months ago

Long horizon tasks are seriously impressive!!

Nagasai (✱,✱)3 months ago

😎gaxis

Death_godd3 months ago

These kinds of development logs are underrated. They give the community a much clearer picture of what's improving under the hood than a simple announcement ever could.

Iknows (✱,✱)3 months ago

Can't wait for the real-robot transfer results gAxis

seven7橘子 🍊 ./ (✱,✱)3 months ago

数据质量和任务难度同步扩张

Shabbir alam 🚢3 months ago

👌👌👌👌👍

Vokay (✱,✱)3 months ago

Great .. Keep building

Qtea_Thunder (✱,✱)3 months ago

hoping to fixed some laggy task

Million3 months ago

Good

𝑻𝒂𝒊𝑻𝒓𝒂𝒏 | π3 months ago

Many features users are looking forward to are coming soon.

jakNtyr ./3 months ago

I love it

Base Degen3 months ago

Cool

𝗞𝗿𝗶𝗽𝘁 𝗛𝗲𝘅𝗽𝘂𝗹𝘀𝗲3 months ago

The pace of development has been impressive lately. @axisrobotics

Marco (✱,✱)3 months ago

Here are two representative

Liam Nguyen (✱,✱)3 months ago

Impressive work on articulated objects and long-horizon tasks! Scaling data volume, quality, and difficulty together is powerful. The simulation examples look great, excited for real-world transfer!

Hoav_Hkeyd | 𝔽rAI | 🌶️| (✱,✱) |3 months ago

So difficult But i like

King (✱,✱)3 months ago

The scaling infrastructure for embodied AI is here. 🦾 @axisrobotics

Asuka3 months ago

Super knowledge. Keep building

Redrose3 months ago

From Simple Actions to Complex Autonomy

KorexKrypto3 months ago

Amazing 🦾

Tagumpay 🎩💎3 months ago

nice

BLACK `🫦' SCP3 months ago

Impressive progress! Scaling complex robot skills and data is game-changing.

𝐑i𝐳z𝐥e𝐎n𝐗3 months ago

It'll only get better from here

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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.

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202,945 views • 1 year ago

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:

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38,959 views • 10 months ago

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22,518 views • 5 months ago

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

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Jim Fan

332,238 views • 2 years ago

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

20,968 views • 1 month ago

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

80,792 views • 1 month ago