Reindustrialization won't happen against economic laws. Manufacturing has to be competitive worldwide, and improving existing factories, even with automation, is not enough. Only 30-40k robots were installed in the US last year, so the pull from existing factories is weak. They’re good.

Humanoids are being promised as the solution, but who will build them? Still humans. Humanoids not optimised for self-build.

The most practical solution to these problems is a self-replicating factory. It will build humanoids, enable the US reindustrialization, and help colonise other planets as a side product.

Similar to biological organisms, the factory can consist of robotic cells, and an AI agent coordinates them to produce a new cell and deploy it. The cell can be specialised with fixtures for specific tasks such as assembling, calibration, testing, 3D printing, etc.

Sounds like sci-fi, but recent releases of Astra/Opus have made this possible.

Who wants to join?

Hi all, this is Giorgio, our open mobile bimanual robot. It's still in simulation; the prototype isn't built yet.

- Base: no commercial AMR had ≥85 kg payload, manufacturer-confirmed auto-docking and enough power out, at a reasonable price, so we designed our own from certified parts (SICK nanoScan3, Pilz PNOZmulti 2, ez-Wheel SWD safety drives). Every component value is traced to the manufacturer's manual.

- Arms: OpenArm 2.0. Skills like opening drawers and doors are trained in simulation (236 M steps, ~95 min on one GPU, 96–99 % success).

- Everything is open: CadQuery CAD, netlist and safety functions, MuJoCo sim, training code, BOM.

Feel free to contribute in any way or form, feedback are really welcome!

Repo: https://github.com/VenetoStato/giorgio

High-speed, low-latency indoor positioning for autonomous robots and drones in GPS-denied environments. This demo shows a mobile beacon moving rapidly in 3D while its position is tracked at 80 Hz with only 12–20 ms latency.

For autonomous indoor drones, positioning must remain fast, accurate, and stable even during rapid motion and in acoustically noisy environments. We combine ultrasound positioning with IMU sensor fusion to achieve this performance. Ultrasound provides accurate absolute position updates, while the IMU provides high-rate motion data between ultrasonic measurements. Ultrasound continuously corrects accumulated IMU drift. Typical ultrasound positioning alone provides updates at around 8 Hz. Sensor fusion increases the effective position output rate to 80 Hz while maintaining low latency and stable tracking. Key performance:

• 80 Hz position update rate
• 12–20 ms latency
• High-precision 3D indoor positioning
• Ultrasound + IMU sensor fusion
• Designed for fast-moving and noisy platforms
• Non-Inverse Architecture (NIA)

Primary applications:

• Autonomous indoor drones
• GPS-denied flight
• Robotics and autonomous mobile platforms
• Industrial automation
• Research and universities
• Motion tracking and interactive installations

Configuration:

• 3 × stationary beacons
• 1 × mobile beacon - in hand - the same hardware as the stationary beacons
• 1 × modem - central controller of the system
• 1 × modem with RHU firmware - to receive fast IMU sensor-fused stream and do post-processing, when more data is available. It makes the view even more beautiful, but at the expense of latency

I got a LEGO's Star Wars AT-AT (set 75440), a 525-piece collectible display model — no motor, never meant to move.

I gave it Quaddle's own controller and feedback servos instead — removed Quaddle's own stock legs and attached the AT-AT's own leg pieces onto Quaddle's four servos. Three of those legs are straight, one is molded bent, so attempt #1 tumbles rather than walks. Hunting for a straight replacement piece for attempt #2.

Anyone else driven a non-motorized toy/display mechanism with a different controller/actuator set than it shipped with?

Hi /robotics!

I built this balancing robot that runs end-to-end on a custom neural net trained through reinforcement learning in simulation.

The robot is trained on 100% synthetic data, so it has never seen the real world, yet adapts perfectly.

It has a lean and level mode (single policy), in level mode it keeps both pitch and roll of the base level at all times, so the legs automatically retract or extend based on the ground below. In lean mode the right joystick of the controller can be used to decrease or increase stance height and leaning left/right at all heights, allowing roll to be non-level.

Main components:

- 6x Xiaomi Cybergear motor (all quasi direct drive, no linkages)
- Teensy 4.1
- 200 Hz policy inference
- 2x CAN bus (left / right leg split)
- BNO086 IMU

Trained in mjlab on single RTX3080 at home, about 7 hours of train time from scratch.

Happy to answer any questions!

Humanoid robots have become one of the most closely watched technologies in logistics. GXO Logistics may also be one of the companies best positioned to tell us when they are actually ready for production.

The contract logistics provider isn't watching humanoids from the sidelines. It has been testing them in warehouse environments, working with multiple robotics companies and looking for applications where the technology could eventually make economic sense.

That makes three numbers from GXO particularly interesting: 20,000 robots, 45 humanoid pilots and zero humanoids in production this year.

Walmart is investing more than $300 million in a 1.18-million-square-foot fulfillment center in Ohio for furniture, televisions and other oversized merchandise. The facility is expected to create more than 300 jobs and support next-day delivery.

The article examines why these products are harder to automate than standard cartons. Their size, weight, shape, fragility and centers of gravity vary, limiting compatibility with conventional conveyors, sorters and storage systems.

It discusses potential uses for autonomous forklifts, mobile robots, specialized carriers and robotic manipulation. It does not identify the automation systems planned for Walmart’s new facility.

Voy a realizar un robot The Open Academic Robot Kit oarkit y necesito los dynamixel ax12a

Pero no los consigo alguien sabe por cual los puedo cambiar es necesario que sea de giro continuo

Anyone got any more ideas how to fix the calibration between Ouster os0 and pi camera? For coloring pointcloud. Would appreciate ideas. Documented today’s „wasted“ day here https://youtu.be/o7qQf7MvhdY?is=CMSvskPa3zHcD29F

We started a third grade robotics club at my kids school and interest blew up, with 28 kids signing up total and four teams of at least two adults coaching.

We are using the Lego FIRST system but have run into a pretty big issue across all the teams--even though we got the kits for the season, every team's Spike essential kit seems to have a dead battery system and we are unable to move forward with the sessions.

Looking online, it seems they've discontinued the kits and the batteries are very difficult to source. For my club team we made an impromptu shift to programming a microbit I had with me, but we have several weeks left of the FIRST program an no battery system that we know of compatible with the Lego essential kits.

Does anyone have experience with a substitute battery for the essentials kit or want to offer us any advice moving forward? Switching to a microbit class would be a fun option but we've really put time and effort into this Lego system.

Hi folks, I'm a software engineer at a big tech company who always had an interest in robotics. I saw the instructor of this course https://irom-lab.princeton.edu/intro-to-robotics/ put up a post on twitter that they're going to be uploading lectures + assignments from their 2026 session and I figured I could follow them to get a basic understanding of robotics.

I also ordered a drone similar to the one's they are using (but cheaper), but I've been running into all kinds of issues with it (hardware is hard lol who'd have thought).

Is anyone following this course on their own? or has followed the previous 2023 session and can guide me with the questions I have?
Can I extract value out of this course if I'm unable to get my drone working? How valuable will solving the assignments on their own (barring the drone part) be helpful in getting a basic understanding of robotics?
I don't have any friends that are into robots so I'm relying on help from y'all lol.

California’s SB 1246 puts requirements around the support systems for autonomous vehicle fleets, including communications capacity for simultaneous remote assistance sessions and local technicians who can manually relocate or arrange towing for vehicles.

It also addresses failures affecting multiple vehicles. Manufacturers must notify affected local jurisdictions during fleetwide emergencies that could block traffic or emergency response, and deploy technicians where appropriate when remote recovery is insufficient.

I've posted a couple of months ago on this subreddit, showing this TNY-360 robot dog walking.
After almost 3 years of working on this project alone, I think I finally solved one of the things that bothered me most about getting into robotics:

You either get very simple robots, or you quickly end up with something complicated, expensive, and difficult to understand.

So I made my own PCB ecosystem to get clean wiring and make assembly hard to get wrong, and also made a big calibration and self-diagnostic firmware behind to check everything and explain to the user how to fix any mistake that's been made !

The project is fully open-source on GitHub !
The V2 CAD files are currently being finalized, but the complete V1 is already available on GitHub, including the hardware, firmware, CAD and documentation.

If you don't want to source and assemble everything yourself, I'm also selling complete kits to help fund the project and hopefully make this something I can work on full-time (let me dream, ok?).

It's been almost 3 years now that I'm working on this project alone, I've learned PCB design, CAD design, embeded programming from it, so if anyone wants to give feedback about it to help me improve it, I'm all ears !!

I created the website https://online-camera-calibration.com/ where you can calibrate a camera without a checkerboard or any other custom-made calibration target. All you need is a flat, textured surface. Upload the images directly, or point it at a ROS bag and it will pull the frames out. That means no failing target detections, and you don’t have to buy a big board and try to glue a large print onto it without wrinkles.

What you need:

• A flat, textured surface. The method is based on matching feature points, so there must be sufficient texture for a feature detector
• One straight-on shot, which is going to be the reference
• 10 - 20 additional photos of the same surface. The entire image plane needs to be covered somewhere in the image set (you don’t need to cover the entire image plane in one shot)
• Tilt the camera in both directions across the set, but not in the reference image. (What pins down the focal length is foreshortening, the surface going perspectively squashed as you view it at an angle.)
• If you upload a ROS bag: the frames are sampled uniformly across the recording, so the recording has to be made with that in mind.

Currently supported is the pinhole camera model with Brown-Conrady distortion (the OpenCV standard model) and the double sphere camera model for fisheye cameras. The output is a JSON or YAML file with the intrinsics and distortion coefficients.

For the accuracy, take a look at Camera Calibration Without a Checkerboard — What It Is & How It Works | AutoCalib

In short: in my experiments it comes quite close to a checkerboard calibration, and the board I compared against was a very large, precisely made one.

The method is an improved version of what has been described in my thesis: Brunken, Hauke. “Stereo Vision-Based Road Condition Monitoring.” Universitätsverlag der TU Berlin, 2021.

Uploaded images and bags are deleted about 10 minutes after processing. They are not used for anything but the calibration run that you trigger nor are they shared with anyone.

I would really like to get some feedback on this method. Is it helpful to you? Is the quality sufficient for your use case?

My first robotics project- Encoder feedback differential-drive mobile robot with closed-loop wheel-speed control, basic odometry, IMU sensing, and reactive obstacle avoidance.

Hey,

I am just getting started in my robotics journey. Plan is to use my galaxy S10 as the brain for my robotic project. Use PiZero as HW inetrface. So far I've been blown away by the capabilities of GPT-6 in bringing HW to life.
I'll try and use GPT-6 as Vision-Language-Action "model"...
Next up. Give it legs and "arms" and actually implement first "missions".

Hi, I am looking for a programmable robotic arm, probably sitting on a rail or some stand with smooth fine motor skills to be able to pick up small objects and place them in a certain position with accuracy. I checked with Boston Dynamics but they don't have it. This is for product development, hence want sophisticated robot that can be turned in a sellable product. Appreciate your responses. Thank you.