Hi everyone, I have designed this robot line to pick and place cookies in the trays, there are a total of 12 delta robots from KUKA. I think I have made a few mistakes and looking for a solution. First of all, not all of the cookies can be picked up, i think that is related to the air flow so Im thinking to make the cylinders of the gripping system a bit bigger to let more air through. I also have a problem with oils a debris coming from the sucking of the cookies and still figuring that one out. I did try with other grippers (almost hands look alike to avoid the debris/oil problem) but the problem is that when it picks one cookie it might break other ones as they are coming close to each other on the conveyors. Now with the speed, the robots wait too long to drop the cookies and the technicians that programmed the line say that it can be adjusted but both speeds have to be increased (tray conveyor and robots speed) which frankly I think will not fix the issue. Im a bit lost. If anyone has an idea it will be greatly appreciated. Thank you

Third post about TAKTO ONE, thanks for all the comments on the other two

This is the hand exoskeleton I built for my master's thesis. Last week Hackaday wrote about it, still can't believe it

Everything is open source: CAD, PCBs, firmware. It reads 12 finger joints directly, so you can use it to record hand data to train your own robot hands, or to drive a robot hand

The big missing piece is the thumb. If anyone wants to take it on, design a thumb module and share it. I'll print it, test it on the device and add the best one to the repo with your name on it, so everyone can use it

I'd love wearables to be something anyone can build, not just big labs. That's why I added TAKTO Lite, a version without the motors. They were about 85% of the parts cost, so Lite is around €164 plus the boards

The video is renders from the real CAD, photos of the real build are in the repo.

ok so some of you might remember this. about a year and half ago I posted our robot here (we were called Everbot then) and it got torn apart. it was basically a round body with 2 arms stuck on it, and someone called it a garbage can. honestly... I can see it lol

Then in January I posted the version we took to CES. that one didn't get roasted, it just got "ok but what can it do?" and then crickets. which kind of hurt more tbh

so this time, no feature list. this is it picking up a button and putting it in a tray, zero shot. everything runs onboard a Jetson Orin Nano Super, no cloud. it took 58 minutes (video is 175x lol), mostly waiting on inference, finding the button, and moving around to get in position. slow, but nobody touched it.

NO VC money in this btw. we paid for the design ourselves, some friends and family chipped in, and our partners helped us get it built in 12 weeks.

granted we learned like 1000 lessons from the last 3 versions. stuff that worked fine in the shop and then just didnt once it left. forgot to plan for self charging (yes really). and now the fun new one: figuring out how we can even sell it in the US with the latest FCC ban list. so thats been a journey

I'll be posting more videos of what it can do over the next few weeks. and if enough people are interested I can do a longer write up on the whole journey, the failures included.

now let it rip, reddit

Hi everyone! We’re building Nuvo Vision One, a stereo depth camera powered by a Raspberry Pi 5, and wanted to share where we’ve got so far.

This started from our own need to add depth sensing to our robotics products. The stereo cameras we were considering were expensive for our budget, and we already had a Pi 5 handling compute. We wondered how far we could get by connecting two cameras and doing the stereo processing on the Pi itself.

The setup uses two IMX296 global-shutter cameras with a shared hardware trigger to synchronize capture. The Pi handles image rectification and stereo matching, producing disparity and depth alongside the camera images.

Doing this on an SBC comes with limits. Resolution, frame rate and matcher settings all affect how much work the Pi can keep up with. We’ve reached a useful starting point for our needs, but there’s still plenty to improve. The attached preview video shows the current output, including the holes and noisy regions where matching struggles.

We also built a browser-based interface to make it easier to use. You can preview the images and depth, adjust exposure and stereo settings, choose outputs, and work through calibration. It’s accessible over the local network through Ethernet or Wi-Fi.

One reason we like this approach is that the camera’s compute is also a normal Linux computer. You can modify the image pipeline and run your own applications on the same Pi. The Nuvo Depth software is open source, including the runtime, web interface and SDKs.

We’ve put product photos and web UI screenshots in this album so you can see the hardware and controls alongside the video.

We’d love feedback from people working with stereo vision or robotics. What would you want to test first, and what would make something like this useful in your projects?

GitHub: https://github.com/arputhamips/nuvo-depth

Combines:
Hamilton Jacobi Bellman
Virtual Force Field (VFF) By Borenstein & Koren
Fast Marching Square
DDMR
(More can't tell)

Do yall think i should make this for personal paper? because i feels like these scenario too complex for sharing..like Eikonal equation and HJB got my brain hurts, whole month just for nonsense daily derivative just to adapt logic algorithm

And also shout out J. Borenstein and Y. Koren their papers are crazy good i recomend if yall intrested to collision avoidance system....my bachelor's thesis bunch of focusing extensively from their papers, maybe its old but some of their works like VFF, VFH , VFH*..etc giving rise innovations that might be found in today’s smart cars

Introducing materialize.cc

Host hardware/robotics projects and print them in over 200+ materials (metals, plastics, resin, etc).

Keep all your 3D files, build guide, source code, component links, and wiring diagrams all in one place.

You can also point AI agents at it and have them put together your project, explore materials, and even place orders with your confirmation.

Let me know if you find it useful, or any other feedback! Cheers

I’ve been working on a project that explores whether a robot can get better at a task by changing its tools. I put our recent simulation experiments together into this video.

The basic loop is to define a task, generate gripper or attachment designs, test them in physics simulation, and use the results to guide the next attempts. Some experiments use evolutionary search over tool dimensions; others use AI-generated CAD with simulation feedback. The robot motions currently come from predefined task controllers.

The next goal is to test the workflow physically with an Arctos robot and a 3D printer. My biggest concern is how to reliably remove the printed gripper fingers from the build plate and supports without damaging them.

Has anyone here worked on automated print removal or a similar setup? Suggestions are welcome.

CEO Jan Zizka explains how warehouse robots request human help with items they can’t pick, and why their systems use several layers of fallbacks for unexpected situations.

He also describes receiving an injunction obtained by Ocado roughly 12–13 hours before LogiMAT, which prevented Brightpick from demonstrating Gridpicker at the show. Zizka discusses the disruption to the launch.

Full: https://www.youtube.com/watch?v=2mvarzMkpMQ

I'm building an RGB + iToF setup for room perception and eventually a robot wrist camera. This is my latest project update, with real sensor captures and the failures included.

The setup is a 320×240 epc660-class iToF engineering sample, a fixed Pi camera and an Ouster reference scan. Increasing exposure from 500 to 2,000 µs took filtered jitter from 65 to 6 mm in our test; raw jitter went from 85 to 28 mm. Coverage improved, but near objects saturated.

The harder problem: a straight wall appears bowed 12–14 cm. Three modulation frequencies report 4.14, 3.92 and 3.97 m for the same wall. We checked the viewer projection against the vendor code and investigated lens presets and factory correction tables.

A fitted correction reduced plane RMS from 4.45 to 1.84 cm on that one wall at about 4 m. I don't count that as calibration across the working range. Mixed pixels, optical spill and cyclic error are still being investigated; next are a range sweep against the Ouster and a lens-hood spill test.

The 8-minute episode shows the bring-up and measurements. Narration uses my disclosed voice clone; explanatory simulations are labelled separately from real captures. I'd be interested in experience separating cyclic range error from optical spill in a similar setup.

Hello, everyone. I am building a 6DOF robot arm and I wanted to know how can I reliably mount the nema 23 gearbox on the J2-J3 link. I want to follow the style of the industrial robot were the motor and gearbox are on the joint directly and not via a belt, like in the AR4 robot arm of Chris Annin. Here are the links to how the motor and the link will be before I connect them:

[https://i.postimg.cc/Ss91BQTf/Screenshot-2026-10-21-104434.png\](https://i.postimg.cc/Ss91BQTf/Screenshot-2026-10-21-104434.png)

[https://i.postimg.cc/rm4ZBynZ/Screenshot-2026-10-21-104443.png\](https://i.postimg.cc/rm4ZBynZ/Screenshot-2026-10-21-104443.png)

I wanted to buy harmonic drives or the flange output style planetary gearbox, but they are not affordable for a university FYP. Thank you in advance !!

Hi everyone,

​I’m a Mech Eng grad with 2 years of experience as a Cloud/Software Solutions Engineer. I’m passionate about robotics and just got accepted for a Research Master's (MPhil) in Malaysia.

​My research will focus on Multi-Robot Fleet Management. I’ll be using my cloud, networking, and AI integration skills to control robot fleets, optimize response times, and reduce accidents.

​I don’t have a US/EU passport and will strictly need visa sponsorship to work abroad. The only silver lining is my professor has strong ties to the Japanese robotics industry and could potentially help me land a job there if I prove myself in the lab.

Is this a good move? Will companies actually sponsor my visa for Cloud/Fleet Management roles if I try to push on that field, or is the market just too tough right now?

Hello i am a curious inventor who just came up with a big new idea! I will call it the Virabot and it will be my robotic companion who is smart enough to help with almost any task and small enough to fit on my shoulder and or pockets. Though as an inventor with a task like this, i don't know where to start.

Please help me make this little helper come to life in the comments.

Powered by the new AgenticROS Trick or Treat Skill, Bob can interact with trick-or-treaters, give instructions, and make sure nobody gets too greedy with the candy bowl! 😂

🛠️ What's powering Bob?
👀 RealSense D457 GMSL Stereo Depth Camera — Bob's eyes
🧠 NVIDIA Robotics Jetson AGX Orin — Bob's brain
⚡ AVerMedia AVerAI SenseEdge Developer Kit — Bringing it all together
🤖 AgenticROS — Giving Bob agentic AI skills and personality!

I recorded a quick video of Bob trying to convince me to take just ONE piece of candy from the bowl at his feet.

His final warning? 😂
🗣️ "You've been warned. Too fast! Don't charge! Take one."

Apparently, Bob takes Halloween candy security VERY seriously! 🍬🚨

This is what makes Physical AI so much fun — combining perception, edge AI, robotics, and a little personality to bring robots into the real world.

Hey everyone!

I’ve been working on a little board that gives basic pwm servos a sense of touch. Here’s a little video demo showing it off. I’m hoping to get the board in the hands of others for some testing, so if you think you’d have a use for a little module like this please dm! I have 5 of them currently built to pass on for testing. A little info on the board:

- Measures current going to the servo to estimate force
- Status led shows whether an object is picked up or not
- Teach button: press and hold the button when the object is held like you want it, and the module will remember how much to squeeze for next time. Works great for softer objects that could break if you close too hard
- Open-drain status output to communicate with your existing MCU when an object is gripped/not gripped

The video shows the servo closing on a box, and then when the box is dropped the board detected it and tells the MCU. It also prevents the servo from closing too hard and overheating.

Lmk what you think and if you’d be interested in trying one out!

Try it: https://holocron-engine.com

A few weeks ago I posted a quadruped designed in Holocron Engine. Thanks for all the feedback, a lot of it went into version 2.

The idea: one tool that connects the parts of a robot that usually live in separate programs. You describe what you want to build, and it goes through the same steps you'd go through by hand:

• Concept: turns your description into a 3D form of the robot
• Actuators: picks real servos, motors or QDDs from a catalog and checks torque, speed and power margins against the load
• Electronics: designs the wiring network pin by pin, then checks voltages, current, wire gauges and grounding
• Layout: places the parts in a 2D plan; moving something in 2D moves it in 3D too
• Structure: builds the frame, mounts and cable routing inside the body, from real part dimensions
• Code: writes firmware for your board that actually drives the actuators, not a TODO skeleton
• Printing: gives you the list of parts to print and to buy

The AI makes the design decisions, and plain algorithms check the facts (numbers, fits, connections). When something doesn't add up, it tells you instead of hiding it.

The video is a mobile manipulator going through all seven steps.

It's still early and some steps are rough. If you've built robots: which part of the process would you actually hand over to a tool like this, and which would you never trust it with?

Honest feedback is the most useful thing you can give me, good or bad. Leave a comment here, or if you try it, hit the Feedback button in the top bar of the app. I read every one, and most of v2 came straight from comments on the last post.

Hi everyone! I’d like to share an update to my locomotion controller for a quadruped robot I designed myself. For now, the video shows it in MuJoCo simulation.

The control loop can now use depth camera images to build a height map. The algorithm is based on D. Kim’s work Vision Aided Dynamic Exploration of Unstructured Terrain with a Small-Scale Quadruped Robot. Using this height map, the footstep planner selects safe footholds during locomotion. This makes the robot more stable and allows it to overcome more challenging obstacles. Moreover algorithm now is able to adapt the stride height dynamically to the height of obstacles. So when walking on flat ground, the robot doesn’t need to lift its legs high, which saves energy and makes the motion look more natural.

Stack: C++, Pinocchio, MuJoCo, ROS 2, MPC + WBC

For now, the controller assumes rigid, non-slippery terrain. As a next step, I plan to improve the reference generator to further increase stability and terrain traversability.

Here’s the link to my repository: https://github.com/voltdog/mors_quadruped. I’d be really glad if you find it interesting.

If you’re developing your own locomotion controllers, please share your experience and tell about your control approaches.

Cubic doggo reinforcement training to stand while minimizing pitch and roll using Gymnasium with PPO from SB3 in MuJoCo, deploying the ONNX file back to MuJoCo.

The first half is with the normal stance, which can already withstand a horizontal force (red arrow) of 3.5N (shown in the telemetry at the top right). The second half is with the specialized "push recovery training", i.e., training with horizontal force applied as domain randomization on a curriculum schedule. The improvement is so tiny, from 3.5N to 4N, after all 2 more weeks of effort, lol

Well, the next step is to balance on a fixed slope and then on varying slopes, deploying to Gazebo and then to the physical robot.

GitHub: https://github.com/SphericalCowww/CubicDoggo_06Z
Music credit: https://pixabay.com/users/retro-bgm-chan-55246343/

Today, l bulid a SLAM camera with myself. it's a bit difficult for a slam beginner yet. It take me about a month to get it working.And the camera is a fisheye, 193.8°, the board also have a imu. l do a fast motion tracking test today, it's works well. Any suggestion or advice? it will help me a lot.

Robot hand 6dof with servo motors
🤖 Robot hand 6dof with servo motors・ STL File for 3D printing・Cults

A 6-DOF robotic hand adapted from the open-source HRI Hand design to fit servo motors. It combines a rigid PLA structure with flexible TPU fingertips for softer contact and improved grip.

Suitable for robotics projects, education, and grasping experiments. Includes editable STEP files to customize the design and integrate it into your own robot.

Digital files only—servo motors, electronics, and assembly hardware are not included.

MARL has been shown a lot in video games and simplified simulation environments, but can it be deployed on actual robots for complex real-world industrial applications?

In this work, we trained decentralized MARL in high-fidelity simulation using Isaac Lab/ Isaac Sim to handle joint task assignment and navigation for multiple robots tending to multiple production machines.

We built a full ROS2 deployment pipeline and transferred the policies zero-shot to the real robots.

Paper: https://arxiv.org/abs/2609.14567

Videos of the real deployment are here.

If you are working on something similar, please let me know; I would love to discuss.

Hi r/robotics, we're building Asimov 1, an open-source humanoid robot. Today we're sharing the updated version of Asimov 1 User Manual.

It's a step-by-step guide that takes you from a bench full of parts to a robot that stands up and walks. It's with 3D visuals you can rotate and inspect in every assembly step.

Asimov 1 is built by off-the-shelf parts, so you can source the components yourself and build one from scratch!

We also made a little old-school film to go with it!

• User Manual: https://docs.menlo.ai/asimov/1/overview
• GitHub: https://github.com/menloresearch/asimov-1

Happy to answer questions, and love feedback on the manual!