We're improving the locomotion policy to make Asimov 1 walk better.

Today is an event day for Asimov 1, and we'll have plenty of chances to show it to the crowd in person!

If you're in Singapore, come visit us at SWITCH (it's a free event).

How can I add more botany and make this move faster?
This is my first time created an ROV machine or anything revolving a STEM competition in general.

It currently moves super slow and sinks all the way to the bottom.

Any advice?

Another Watti update!

Some of you have been following this project for a while, and today I have something exciting to share!

I've temporarily opened Watti Studio to everyone!

You can now create your own animations directly in your browser, on your computer or phone. Move the virtual robot, add keyframes, adjust the timing, and bring your ideas to life.

And the coolest part? Your animations can be performed by the real, physical Watti!

The system is still under development, so videos from the real robot may arrive with some delay.

You can try it here: https://studio.watti.dev

I'd love to see what you create! Feedback and suggestions are always welcome.

I need to make it move faster and easier through obstacles.

As of now, it sinks to the complete bottom, and is hard to navigate. It’s also too slow to win in a competition

https://reddit.com/link/1xga61e/video/ikm4cp0gsvxh1/player

I built an SDK that let's you connect any LLM/agent to any SO-101 or bimanual YAM set up! The demo video shows Gemini 3.7 trying to "stack the yellow blocks" (sped up 10x).

The idea is pretty simple: you give the robot a small job, like sorting blocks by color or stacking one on another, then a model can take one step at a time, or an agent can plan smaller tasks and check its work along the way. There’s still a lot to improve, especially for reliability, but I'm pretty happy with it.

Technical specs

• Python SDK
• Support for SO-101 and bimanual YAM arms
• Local MuJoCo simulation if you don’t have a robot
• Claude, GPT, Gemini and other frontier models through one API key
• A judge scores each subtask and returns timestamps and failure types so you can inspect where a run went wrong
• The connector runs locally and enforces motion limits that the model can’t override

The model and the execution method are separate settings, so you can switch models while keeping the same setup, or see whether a little more planning helps with the tiny pile of blocks.

I put the SDK online if anyone wants to give feedback or try it out (for free) themselves!

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.

Latest progress on our collaboration with the University of Waterloo. Hopefully we'll see it come to life by the end of this year!

Just showing off our steering and lego components, we also show the rgb array doing "chase" and turning on the headlight. Sorry I don't have a side by side with the browser driving, but we have other videos of that.

Thanks for your views!

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.

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

Day two with the ECO65 on my desk. I used the arm’s motion to calibrate an iPhone on the wrist and an external iToF sensor, then put the RGB-coloured depth cloud and joint motion into my office twin.

For the iToF fit I averaged 10 frames per pose, isolated the pixels that changed when the arm moved, and fitted those points to the robot model with ICP. Median fit error was 7 mm; that’s the fit residual, not an independent accuracy measurement. J3/J4 didn’t give enough visible motion to help much.

The end shows a real 1%-speed run, with the recorded RGB/depth/IR/joints replayed together over the room splat. Phone-estimated depth is still unreliable: only 6/20 frames passed the check.

Full build and failures: https://www.youtube.com/watch?v=HMC7yu18qdQ

If you’ve calibrated a fixed depth camera this way, how did you choose poses that made the fit more robust?

Has anyone here seen a single actuator able to extend and rotate a joint in a robotic arm . I have seen multiple iterations of cycloidal gears being used for a robotic arm for its torque , would that be a good method to compensate for the increased weight that the extended portion of the robotic arm would induce in each joint, having the former joint each needing a greater torque power.

Please 🥺 help this poor soul needing guidance

Small production runs can be expensive because programming and setup costs are spread across fewer units. For chemistry-based products, changes to a formula can also require changes to batching and validation.

The article looks at using AI with robotic batching to reduce that setup work, allowing manufacturers to produce smaller quantities of more product variations. The proposed benefit is lower minimum orders and less unused inventory.

The approach is still early. A public demo is planned for Q1 2027, and questions remain about production reliability, regulatory requirements, and protecting customer formulas.

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.

This is our humanoid robot, dual arm, each arm 7 dof made with stepper motors and servo motors

Head with depth camera Intel realsense

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