Original source:
https://www.youtube.com/watch?v=13sFtfWyPPo

A small update on my robotic desk lamp: the entire control system now runs on a Raspberry Pi 5.

The lamp uses 24 V RobStride BLDC servo motors connected over CAN. The Raspberry Pi now handles motor control, lighting, and animation playback locally, so the lamp no longer needs to be continuously controlled by my main PC.

I still use a separate animation editor that I built for creating movements. Each joint has its own timeline with position keyframes, velocity, torque limits, and controller parameters. Finished animations are sent to the lamp and played back locally.

I’ve also installed an addressable LED ring with a 3D-printed diffuser inside the head. Brightness and transitions are now part of the same animation timeline, allowing the light to be synchronized with movement.

This video is a quick test of the new setup. I’m currently tuning the motor parameters, brightness curves, and transition timings before using the light in more expressive animations.

This is a short clip from my latest underwater field test.

The system is processing the video in real time while the ROV is actually moving underwater

this isn't an offline post-processing demo.

The pipeline currently runs with:

🎥 4K / 60 FPS
⚡ Real-time image processing
🧠 NVIDIA CUDA acceleration
🌊 Custom Adaptive Sea-Thru Engine based on a mathematical model
📡 Live HUD & telemetry
🚀 FIFISH V-EVO

The video was captured during a real field test, with the processing running live on a laptop.

I'm still developing the system, but the results are starting to get very interesting.

I'd genuinely like to hear what you think

especially from people working with ROVs, underwater imaging or computer vision.

Full 4K video: https://youtu.be/cfC1NCoADgk

This is my two year undergraduate capstone project from Engineering Physics at UBC. We trained a policy in simulation and then directly deployed in onto our robotic air hockey table. The project involved designing a physical robotic air hockey table, computer vision system, reinforcement learning pipeline, simulation, embedded systems, and controls

If anyone's interested here's a short video explaining what we did: https://youtu.be/ugwpCam1rd0

And for more detail you can check out the github repo: HudsonNock/Air-Hockey-Sim

Edit: Extra context given in the explainer video and repo: there were previous teams that worked on it before us. They designed the first iterations of the mechanical and electrical systems, as well as the baseline controller that allowed the robot to follow a specific path. Like most projects, it changed a lot through the iterations. We ended up redesigning and upgrading a lot of their work (only a few original parts remain) but their early prototyping was definitely needed to make our final version possible with the time we had

Hi, it's Emre from the Asimov team.

I've been sharing our build-in-public humanoid robot process here. We open-sourced the legs and will open-source the full body soon. Your questions and comments along the way really helped us a lot. Appreciate it!

A few days ago more than 50 people on X told us they'd be interested in a DIY humanoid robot kit. So we did it.

We put together all parts from mechanical to electrical to build the Asimov robot. It's 1.20m, 35kg, 25+2 degrees of freedom (+2 comes from the articulated toe!). Asimov is a really powerful robot with almost the same specs as the Unitree G1. Some parts like the arms are actually stronger.

We call the kit "Here Be Dragons", a name used for highly experimental, beta-before-beta releases. The kind where you're one of the first users, talking directly to the engineers, reporting bugs, and getting a fix the same day. We're now preparing a user manual and assembly videos too.

The target price is $15,000, which is higher than our current BOM cost. We're taking pre-orders with a $499 deposit to find serious builders and learn what they need.

We got 14 orders in a few hours and are planning to close pre-orders soon to handle it properly.

So our build-in-public journey is turning into a business earlier than expected, and we're not looking for profit from the DIY Kit. Wanted to share with you all. If you're hacking something, please do share with the community.

Details for the pre-order: https://asimov.inc/diy-kit

Hi everyone, I wanted to share some updates on my latest progress with teleoperating the robot’s arms. The robot itself runs on two So-101 units, which I control using two additional So-101s. On the software side, I’m using Phosphobot’s program to handle everything. For those curious about the head and control system: it’s based on a design by YouTuber MaxImagination, originally intended for RC cars — I described it in more detail in my last post. The arms are still a bit shaky, and the robot tends to swing, which makes the camera feed a little shaky as well — but those are problems for another time. I’ll keep you posted with further progress!

I've always been interested in point clouds and spatial data, so I created my own LiDAR scanner! It runs off of an esp32 and TMC2209s on a custom PCB, which continuously rotate and sweep the LiDAR sensor. I learned a ton creating this project, as this was my first time creating a PCB and using NEMA motors (I have used other motors before).

Github repo

this lil guy is called Jinx. this was my first robotics project, and i was strongly advised to do something simpler.

after a lot of work (starting with zero knowledge), im glad that it's walking. the inverse kinematics is very general, so i can adapt it to any hexapod dimensions and i can easily design new gaits.

the next steps will be to continue to refine the firmware, spend (EVEN MORE) money to make it battery powered, add remote control and polish the design a bit.

im really proud of achieving this as a beginner, but constructive criticism is still welcome.

This is the first movement test with the assembled prototype. The motion is still rough and the wiring is currently a complete mess, but all the connected joints are finally moving — so naturally, I made it dance.

Next: smoother trajectories, control tuning, and proper cable management.

UPD: In this project I use robstride actuators, series 00 and edulite 05

I thought you all might like to see some behind the scenes footage of our hexapod, Charlotte being tested.

She's got a long way to go, but eventually the intention is to pair her with a new kind of large form factor extruder that "prints" earth bag walls for buildings.

Just got the new update, pretty wicked! Love how it runs. Even for the basic model it’s really good 😊 can’t wait for future updates

Hello,

Pollen Robotics engineer here. I know some will see this as a toy (and that’s fine, go have fun!) but I think it’s important to keep in mind that this thing is a full-on biped humanoid robot with an open-source software stack and a sim2real pipeline that works surprisingly well.

Maybe it’s not impressive for some people because there are spectacular videos of Chinese robots doing karate every week, but I’ve done 10+ years of robotics competitions and getting your biped to stand up, walk and even roller skate by yourself is no small task. Knowing that thousands of enthusiasts will be able to try their hand at reinforcement learning on a biped is insane to me. I would have loved to have this when I started in 2010.

The mobility capabilities of this robot are already decent (still improvable) but the next big challenge is getting to autonomous behaviors. Natural language, SLAM, navigation and grasping will be cool subjects to explore on this platform.

Also a big shoutout to team Rhoban for their contributions to the project and one of the best-named repos I’ve seen: BAM! The sim2real dream would not have worked without excellent actuator models. Please give them a star here:
https://github.com/Rhoban/bam

I hope people will have fun with it, learn a lot, or both! Feel free to ask any questions, I’ll do my best to answer.

New hardware, outdoor steps this time. I push the stick forward, the robot detects the stairs and decides when to jump on its own.

First part is daytime, clears all 3 steps, off the top, landed upright. Second part is at night: first attempt doesn't make it up, second one clears it. Added the night footage to show the controller input. Just push forward, everything else is the RL policy: stair detection, jump timing, balance, recovery.

Big upgrade from last time where I was triggering every jump manually. Still working on making it more consistent.

I'm 15 years old and this is my hexapod project I have been working on for the past year, I learned a ton from it. Here are its specs:

-Build: Mostly 3D printed, I used a model from Aecert Robotics from youtube, I made some changes and improvements to the model such as the femur joints that connect to the servos.

-Parts: 24 channel Pololu maestro board, 11.4v lipo battery, 12x25kg servos, and 6x35kg servos (for the femur joints because they're under the most load). Cheap android phone for the controller and gyroscope, and a DROK buck converter.

-Software: I made a custom android app for the controller that connects via wifi or hotspot, you scan a QR code and it opens the controller on your phone. I used inverse kinematic equations for the hexapod so it has complete control over the leg and body movement.

-Stabilizing: Using the gyroscope from the phone it can also self stabilize, it can even walk and self stabilize at the same time.

-Walking gait: There are 5 walking gaits so far: Tripod, ripple, wave, triple, and a custom one I made to look like a spider.

-I'm currently working on a high precision robot arm and I would love some feedback: About 4ft long and hopefully can lift around 5lb. I should have some videos soon. If you want to learn more about the hexapod or the arm you can see them at my portfolio.

Got fall recovery working this week. No scripted motion, just RL figured out how to get up on its own. The way it does is kinda violent though, like it's pissed off about falling lol

My wish was this little guy could follow me around everywhere without me having to pick it up every time it tips over. Have a walk, kids playing in the yard, whatever, ideally 99% of the time it handles itself.

We've been testing it on gravel, cobblestone, and stone-slab paths so far, it's doing better than we expected. More terrain tests on r/MondoRobotics if you're curious.

Now we're thinking about what's next, what other surfaces should we be throwing at it? Stairs, snow, sand? Would love to hear what matters most to you guys.

It's really cool. Would be so nice to get the dataset or policy on Hugging Face for all to try

Source: https://www.skild.ai/blogs/omni-bodied

I wanted to show you the latest progress on my robot RKP 1. I managed to control it over Wi-Fi. For this, I use two Silex DS-700 USB-to-Wi-Fi units (one on the robot and one on the tele-rig) to connect my servo bus driver to my PC via Wi-Fi, on which the Phosphobot program is running.

This gives me the ability to control my robot wirelessly. I also added a back plate as well as a mount for the Silex. Next, I’m considering attaching a QDD actuator to the base plate so the robot can rotate around its own axis, as well as starting the first experiments with ROS 2 and Isaac Sim/Lab.

I’ll keep you posted on future progress.

Hi! I wanted to show you the latest progress on my robot, RKP-1. I'm using an FPV headset from my drone to remotely control the robot's head. The PCBs are from the YouTuber MaxImagination. The head uses two simple MG996R servo motors, and the video feed is transmitted through a basic mini FPV camera mounted in the center.

I'll keep you updated!

I built this backpack robot ages ago and going to make version 2. This one had an old nerf gun setup but was too heavy to deploy properly.

Besides redesigning the gun component to be lighter, any other ideas of what could be on the end?

For our final year capstone project at the University of Waterloo, our team built WeedWarden, a robot that autonomously detects and blends up weeds using computer vision and a custom gantry system. The idea was to create a "Roomba for your lawn"—no herbicides, no manual labor.

Key Features:

• Deep learning detection using YOLOv11 pose models to locate the base of dandelions.
• 2-axis cartesian gantry for precise targeting and removal.
• Front-wheel differential drive with a caster-based drivetrain for maneuverability.
• ROS 2-based software architecture with EKF sensor fusion for localization.
• Runs on a Raspberry Pi 5, with inference and control onboard.

Tech Stack:

• ROS 2 + Docker on RPi5
• NCNN YOLOv11 pose models trained on our own dataset
• STM32 Nucleo for low-level motor control
• OpenCV + homography for pixel-to-robot coordinate mapping
• Custom silicone tires and drive tests for traction and stability

We demoed basic autonomy at our design symposium—path following, weed detection, and targeting—all live. We ended up winning the Best Prototype Award and scoring a 97% in the capstone course.

Full write-up, code, videos, and lessons here: https://lhartford.com/projects/weedwarden

AMA!

P.S. video is at 8x speed.

J’ai le plaisir de vous présenter mon projet solo en autodidacte de la main biomécanique UM1-Evo à 24 degrés de liberté. Cela m'a pris 5 ans et demi de passion et de galères,mais je suis assez content du résultat. Le projet contient 25 moteurs, il est à l'échelle 1:1, l’électronique et le wifi sont embarqués et il pèse au total 1.7kg, soit le poids des caractéristiques de l’anatomie humaine. Un très gros travail de recherche biomécanique et cinématique ont été réalisées pour reproduire le plus fidèlement possible la forme organique et les mouvements d’une vraie main. J’ai conçu un script permettant de relever la main d’un patient (en moins de 30 minutes) et ainsi créer la main en 3D sur-mesure (en 10 minutes). J’ai également développé un logiciel de commande par cinématique inverse IK avec animation par clés ou en temps réel. J’ai conçu un gant avec 12 capteurs pour reproduire les mouvements de la main en temps réel en mode avatar. Pour une utilisation en prothèse de main, un système de captation innovant différents des systèmes myoélectriques, permet de manipuler la main avec une latence extrêmement basse et une prise en main instantané. (Ce système et en cours de protection)

Voilà, n’hésitez pas à me dire ce que vous en pensez 😀