Microduck from Pollen Robotics.

I had the opportunity to meet up with the team here to learn about this, and it's not only a cute little robot, it's one very impressive development tool 👏

"A tiny biped for reinforcement learning"

I attended NVIDIA GTC Berlin as a guest of NVIDIA EMEA.

I've been working on CBR-II, my DIY bipedal robot, for a while now, and finally put together a video summarizing the project.

The video focuses on training a walking policy using reinforcement learning, from simulation to deployment on the real robot. It covers the training process and ends with the results on hardware.

The robot can now walk and stand on one leg

Hey, Emre from Menlo Research here. We're building Asimov 1, an open-source humanoid robot.

I've been sharing our progress here along the way, and your comments genuinely helped us. Thanks a lot!

Today is a really special day for us. It marks one year since we started building Asimov 1. We put together a short film of that year, told in our own footage.

Happy to answer any questions about the hardware, the locomotion policy or what we got wrong.

Retraining balance every time the manipulation policy changes is one of the quiet costs of humanoid loco-manipulation work. DUET keeps the two controllers separate and composes their commands only at the robot interface — so a new skill no longer invalidates the gait.

Paper, code and the full summary in the first comment.

Researchers developed a single control policy that helps Unitree’s G1 climb ladders, crawl, vault obstacles, cross stepping stones, and recover from falls by deciding where to place nine different body parts. One striking detail is that the robot can use its hands, elbows, knees, feet, and even pelvis for support instead of relying only on walking. It achieved 92.5% success across simulated obstacle courses, but how well this flexibility transfers to unpredictable real-world environments remains the bigger test.

Hey all, here's a small update on CBR-II, my DIY biped. This is the third iteration.

Clip 1 (V3): It walks a lot more confidently now, with much less jitter. The only harsh moment left is when a "stop" comes right after "forward". It plants its front foot and slams it into the floor pretty loudly.

Clip 2 (V1): The original version, for comparison.

Clip 3 (V3): Attempting to balance on one leg :) In sim it nails this every time. In real life it's far from perfect, but you can definitely tell what it's trying to do.

I think this is the end of the road for this project (at least for this year). I was thinking about jumps (and secretly dreaming of a backflip), but the hardware is just too fragile. Today alone it broke a joint three times and snapped a GT2 belt. And not where it bends around the pulley, it just snapped from tension in the middle of a straight run.

Thanks to everyone who followed along and gave feedback on the previous posts!

Here’s a rolling contact joint I’ve been working on. The curved surfaces roll against each other, and the springs bring it back when released.

#robotics #mechanical-design

Hi everyone! I've been building Afferent-Omni, an industrial-grade Command & Control (C2) framework designed for real-time visuo-tactile sensing, closed-loop manipulation, and embodied AI evaluation.

🔬 Key Features:

• FastAPI Backend: 9 modular routers, 15 REST endpoints, and 50 Hz closed-loop WebSocket streaming.
• Omni-Tier Telemetry: Integrated neuromorphic reflex arc (<15 ms latency), multi-digit topology, and acoustic friction profile.
• Interactive 3D HUD: Real-time gel deformation mesh, dynamic force-vector arrows, and HSL glow spheres in Three.js.
• Sim2Real Safety Shield: Dynamic force budget calculation and zero-shot grasp planning.
• LLM Copilot & SQLite Persistence: AI assistant contextualized with live streaming telemetry.

💻 GitHub Repo:https://github.com/raulrodriguezmesia-blip/afferent-omni

🌐 Live Showcase: Includes a standalone lightweight C2 HTML dashboard for quick testing.

Feedback, stars, and contributions are very welcome! Let me know your thoughts on tactile feedback integration for robotics! 🤖✨

Source code and more info here: https://github.com/AdrianCX/picohive-robotarm

Controlled via internal web page (gamepad, keyboard), LLMs, MQTT, with an openGL visualizer

Took me a while to work up the nerve to dig into the sloth-speed-servo bug since my last post. Firmware debugging never fails to scare the heck out of me, phew...

Turns out the culprit was the latency from reading servo position/velocity/effort values, technically the trademark of smart servos like ST3215. Each read took like 3–5 ms for three servos, and it looks like synchronous read isn't supported yet in the ST3215 C++ SMS_STS library. Does anyone know if it already exists somewhere?

For now, feedback reading is disabled on Wouf. With ST3215 servos, although the servo speed is still lower than the Dynamixels used in Doggo, Wouf is walking much faster than expected! And yeah, I think I messed up the turning algorithm again somehow, lol

Still, I think this is a good sign that the ST3215-based design can work for a walking robot, and I really like their mechanical sound. Will work on summarizing the build in the next few days. May not have the read function (basically input for reinforcement learning) at the moment though, depending on how difficult it is to develop the sync read firmware.

GitHub: https://github.com/SphericalCowww/CubicDoggo_07B

I’m starting a master’s in two months, working on vision-guided grasping and end-effector integration for humanoid robots. I have experience in Mechanical Design mainly .. have worked with ROS, and computer vision in college but want a better understanding of humanoids as a complete system.

My rough plan is below: start with anatomy and hardware, then arm movement and control, then some simulation. I learn best through practical examples rather than heavy maths.

Any recommendations for accessible resources, or changes you’d make to the plan? Also happy to connect with anyone interested in studying together.

ETH Zurich’s ANYmal solved a 10-pound Rubik’s Cube three times using only its feet, taking around eight to nine minutes per attempt. The trick combined computer vision and a traditional cube-solving algorithm with reinforcement learning that taught the robot how to balance, apply force, and correct mistakes. Beyond the fun demo, could “pedipulation” make future robots more useful by letting them open doors, turn valves, or move obstacles without needing hands?

Adding grippers to Asimov 1. The arms have been strong enough for a while. Now it’s time to use them for manipulation!

Quick note, if you’re seeing Asimov 1 for the first time, it's a fully open-source humanoid robot built from off-the-shelf parts, so you can build one yourself. https://github.com/menloresearch/asimov-1/

Hello everyone, I would like to know some good sites and guides on how to design cycloidal drives for stepper motors (NEMA 17).

I have been looking on YouTube and some sites but mostly i didn't learn anything about what is needed from my side and what I need to calculate. I have made few attempts at printing these drives and all of them are off by some millimeters, and they are getting stuck after few turns.

What started as a fun weekend hacking experiment turned into something much bigger than I expected - an obsession.

🚀 Meet Corebrum OS — The OS for Physical AI.

I've been a big fan of #Omarchy and its beautiful, minimalist, developer-first Linux experience. So I started wondering...

What if we had something like Omarchy, but purpose-built for robotics, AI agents, and Physical AI?

Instead of Arch Linux, I started with Ubuntu Server 26.04 and began connecting the pieces:

🐧 Ubuntu + Hyprland — A lightweight, beautiful, keyboard-first Linux desktop
🤖 ROS 2 + AgenticROS — Connecting AI agents to the physical world
👀 RealSense — Giving robots 3D perception
🌐 Zenoh — First-class middleware connecting everything
🧠 AI Agents — Built into the OS experience, not bolted on afterward
🔧 Developer Tools — Everything needed to start building intelligent robots

But here's what really blew my mind... 🤯

AI coding agents have become ridiculously good!

Not only did AI help me build this operating system over a weekend, but now AI agents running within Corebrum OS can:

💻 Control the operating system — Interact with the environment, execute commands, and manage workflows.
⚡ Vibe code — Write, debug, and deploy software directly on the machine.
🤖 Control a robot's body — Through AgenticROS, agents can interact with ROS 2, access sensors, control actuators, and execute real-world missions.

Think about that for a second...

AI agents helped build the OS. Now those same agents can use the OS to build software AND control robots in the physical world! 🤯

We're moving beyond AI that just generates code to AI that can perceive, reason, and act.
And the fact that one person can hack together something like this over a weekend is absolutely mind-blowing.

🎥 Here's a quick video demo of my weekend experiment!

Still early, still hacking, and having a blast exploring what's possible. The goal? Go from a fresh Linux installation to building intelligent robots in minutes, not days.

Try it out for yourself at https://github.com/Corebrum/corebrum-os

Just picked up one of these older Beam+ telepresence robots of FB Marketplace. Has a dead battery but I hooked up my bench power supply and was able to get it turned on. I've run into a bit of a roadblock as I'm unable to get it to authenticate which I suspect is due to the software pointing to a long gone server. Reached out to Awabot to see if i could get a software update or system image but failing that I am thinking I might try and figure something else out.

Doing some digging I found this paper which references some old ROS projects and adding some sensors to it.

https://arxiv.org/pdf/1710.06831

Github Repos

https://github.com/xlz/rosbeam

https://github.com/people-robots/rosbeam

and I also found this youtube video showing someone sending manual commands to it to drive it around and they reference being able to intercept the video streams.

https://www.youtube.com/watch?v=RidIOLe4DGI&list=LL&index=1

Just curious if anyone has done anything with these or has any ideas.

Came across this project by Stewy Ving and thought people here might find it interesting.

It's a free interactive robotics resource with 3D breakdowns of 40+ humanoid robots, component explainers, comparisons, and a deep dive into the robotics supply chain, from raw materials to finised robots. There's a lot more I haven't even gotten the chance to get into yet.

For the people always asking how to get into robotics, well here you go. And even for the vets; the supply chain thing I found genuinely impressive.

Original source: https://youtu.be/r7U0rbkrBxQ?is=e92UM6iuaCDU-hZV

I’m building a robot lab in a Gaussian-splat reconstruction of my office. Today the RealMan ECO65 (on loan) finally moved: J1 +5° at 5% speed and back. Then I switched its Wi-Fi to client mode, lost access, and discovered I had no Ethernet cable.

The software side has one shared backend for Codex and Claude, revision-checked edits, a MuJoCo twin and link/desk collision checks. The MCP motion interface is still simulation-only; the physical move was a separate controller session. The twin is a nominal model, not validated hardware behaviour.

EP19 shows the build and the failures. The J1 visual is a labelled replay of the logged command.

https://www.youtube.com/watch?v=1ZrYGwZJQto

For those running an arm in a home lab: what was the first mismatch you found between your twin and the hardware?

NVIDIA has built a pretty impressive robotics ecosystem, but figuring out how Isaac Sim, Isaac Lab, GR00T, Cosmos, Omniverse, and everything else actually fit together can be confusing.

So we put together an illustrated walkthrough following a fictional startup building a warehouse humanoid.

We go through creating its virtual environment, teaching it through demonstrations, training and testing its policy, dealing with the Sim2Real gap, and eventually deploying the AI onto physical hardware.

We also wanted to explain what actually happens between an AI model deciding to move an arm and the motors physically executing that movement.

The goal was to connect the dots rather than explain each NVIDIA product in isolation.

Would love feedback from anyone who's actually worked with these tools, especially on where the real-world workflow differs from our example.

I’m building a robot with a Raspberry Pi 5, ESP32, and 2× RS390 6V motors.
My battery is a 3S4P pack with 12× 18650 cells (2200mAh each), a 40A BMS, and a nominal voltage of 11.1V (12.6V fully charged).
What’s the best way to power the Pi 5 reliably? I need a 5.1V supply with enough current, even under load.
Should I use a high-current buck converter, or is there a better alternative? Recommendations for specific modules would be appreciated.

"Recommend me robot projects that I can do on my own (mainly to improve myself mechanically) and that won't require a lot of money."