Igor Kulakov (MicroFactory)
@ihorbeaver • 9,252 subscribers
Building MicroFactory - personal factories accessible to everyone. Seed soon
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Model precision test. Trained with 2 minutes examples.
Igor Kulakov (MicroFactory)322,968 görüntüleme • 21 gün önce
We’re shipping an interchangeable gripper. Our main goal, as usual, is to make modern AI tools work in environments where you need reliability. Humanoid hands have their use cases, but it’s hard to pick up a tool firmly and control precisely with them. We made an alternative - an interchangeable gripper. The tool is fixed to the gripper in advance, and the whole hand is swapped. AI is quite good at generating this kind of custom tooling.
Igor Kulakov (MicroFactory)19,759 görüntüleme • 4 gün önce
Bare imitation learning without robotic pre-training is an unfairly overlooked area. (it doesn't even have a name) When done right, it has the advantages of programming. It’s reliable, predictable, easy to modify and debug. The only difference is that the source of truth is a small dataset instead of code. And it is glued together by a vision encoder and a transformer model instead of being compiled. It's hard to imagine how Claude Code could appear without a ton of production-grade source code hardened over years of real use. Same for robotic models. Here is the robustness of such a model. It was trained on orange zip ties with no lighting changes.
Igor Kulakov (MicroFactory)19,903 görüntüleme • 9 gün önce
Strong magnets can make the environment self-resetting.
Igor Kulakov (MicroFactory)96,094 görüntüleme • 1 ay önce
We have news! We created a new robotics model called Loop Model 1. On the zip-tie insertion task, it achieves 20x more throughput per unit of data than "Pi06 + RLT" from Physical Intelligence, a top model for such tasks. It’s the missing piece that makes MicroFactory work, because now deployment becomes so simple and fast that our users can do it themselves.
Igor Kulakov (MicroFactory)76,135 görüntüleme • 2 ay önce
Here is the full run of MicroFactory, that autonomously assembles a photo frame - a real product sold on Amazon. This is a good example of how to deal with complex tasks, while we do not yet have a large set of robotic data to train a big general ai model. Thread:
Igor Kulakov197,003 görüntüleme • 8 ay önce
Robotic insight 2: One undocumented feature of VLA models is that they can generalize pretty well from very little data, if you run them on good hardware. Here, with 35 demonstrations (5 mins in total), robot can pick up small pcb from any position and at any rotation angle.
Igor Kulakov (MicroFactory)47,571 görüntüleme • 6 ay önce
Threading the needle with our robotic arm. Good autonomy can’t be achieved without a solid arm to begin with.
Igor Kulakov81,684 görüntüleme • 1 yıl önce
How to keep your fingers clean while eating chips? Use a robot! - $250 opensource robot with most advanced AI models inside (Tesla bot and Figure use them too) - 15 minutes of examples - $3 for training in Google Collab - Don't hesitate to give a star to the repo if it's interesting:
Igor Kulakov80,442 görüntüleme • 1 yıl önce
Below is our open-source arm assembled by one of the discord members! Also, today Physical Intelligence shared their incredible top-notch robotics model Pi0. And I just want to remind you that you can use our arm together with this model and with LeRobot The arm has the same DOF as the arm used by Pi0, but has smaller size and cost starts from 200$ per arm. You can use better motors like we did in our demos, then it will be more precise and stable but the price will be about $600 per arm. Printable files, purchase list, and help with the assembly - in our discord channel.
Igor Kulakov53,711 görüntüleme • 1 yıl önce
Robotic insight 3 There’s a widely held view among leading robotics thinkers: the current priority is to automate some commercially profitable work to get real-world data. If you set the goal as “the first profitable deployment that also generates data” you get a few insights: - People with industry experience may be better positioned to nail this than academic researchers. - You can try to find non obvious initial domain, that’s easier to deploy in practice, while still provide useful data.
Igor Kulakov19,659 görüntüleme • 6 ay önce
Milestone! We (robotic arms for gadgets assembly) finished the first commercial order, which brought the first revenue. Here are some learnings from this: The customer was a smart toy manufacturer. The task was to add a heatsink to Raspberry Pi. We received parts from them and returned the assembled modules back. Currently, it's done by teleoperation. Later it will be done by a remote employee via the Internet. Then it will be automated action by action, reducing the operator's time on this and making the task profitable. ps. If you have an assembly task that we can do for you asynchronically - leave a comment below. Learning 1. It's possible! This task which is usually done by the human arm with 5 fingers can be done with a two-finger gripper with the addition of a couple of simple tooling. The task was not simplified. We peeled off thin films from stickers, unpacked paper boxes, moved PCB boards full of components, etc. And no unsolvable problems have been encountered yet. Challenges: 1) The paper box shifted during the opening Solved with the plastic walls that you can lean against 2) Heat pad, stuck to the gripper instead of heat sync. Can be solved by gripper with a pump, but this time solved with the patience of the operator 3) The film on the pad is very thin. Turned out that sub-millimeter arm precision is enough to peel it off with just a regular gripper. 4) The working area has not enough space. You'll only know this by doing real tasks in bulk. This could be solved by an extra pair of long arms, but in this case, solved with the patience of the operator. I think that in the end, we will have 5-10 types of universal tooling and 5-10 types of grippers to solve almost all the problems in such assembly tasks. Learning 2. It's slow. It took 5 times more time, than doing it with human hands. But the good news is there's a lot of room for improvement. We now have specific “time for task” metrics, which we will decrease with iterations. The main reasons for slowness: 1) To rotate the gripper to a steep angle you are forced to control one robot arm with two hands instead of using both arms. We can fix this by just making more room for rotations. 2) Grabbing PCB board with two arms is hard. A slight difference in rotation can break the board, and it's hard to control these angles visually. To solve this, the best way is to use force feedback so you can feel the pressure applied to the item. 3) Accuracy and steadiness is still can be improved We will try a metal version and double the motors to do this. 4) It is physically difficult for the human hands to move with such precision To solve this, we will add a pad for the hands like in surgical robots Learning 3. It's a good business model The "Factory in the cloud" is a good business model for this stage. You send us parts and we send back assembled modules. Currently, it's more convenient than sending a robot to your place, as we can iterate/fix the robot quickly and utilize it 100% of the time. When we polish the set-up over time - we can send robots to your place. So if we can assemble something for you in the USA with Chinese prices by using modern automation - leave a comment below.
Igor Kulakov37,266 görüntüleme • 1 yıl önce
