Err..., so instead of fixing the turning gait code, I already made Wouf carry some groceries. While the ST-3215-C018 servos cannot move very fast, a 1 kg payload is like half Wouf's weight, so I got pretty excited to see it is able to fumble a few steps while carrying the load :D

Wonder what else for it to carry, though. Perhaps its ST-3215 cousin that also uses 12 servos, like the SO-ARM101 🤔

GitHub for the build: https://github.com/SphericalCowww/CubicDoggo_07B

So I own a macanoid robot that is 4 feet tall, and the company has shut down the servers entirely for the software, and I don't know how to operate it. Anymore, since the app is down and I need help on finding a software that might be able to still program it or move it.

An autonomous 4-wheel drive explorer robot based on Arduino UNO R4 WiFi, designed to navigate through paths and mazes while detecting surrounding obstacles. The robot performs 180° radar scans via an ultrasonic sensor mounted on a servo motor and transmits real-time telemetry via Wi-Fi to an interactive Web dashboard, which reconstructs the 2D vector map of the surrounding walls.

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).

RSVP HERE. ​We're visiting NYC and would like to meet the New York ROS developer community.

Join us on Thursday, October 29th, for a ROS Meetup at the Google office on 10th Avenue for an evening of socializing and guest speakers. Food and drinks will be provided.

​Guest speakers include Chris Anderson, VP of Software and HRI at Fauna Robotics, Matei Ciocarlie, co-founder of Tangent Robotics, David Pietrocola co-founder of Cohesive Robotics, and Kat Scott developer advocate at Intrinsic.

​If you would like to pitch your startup or open source package please reach out! We would love to hear about what you are working on.

This is a small RL project I've been working on. A simulated Franka Panda learns to pick up a cube and put it on a target pad, using PPO from Stable-Baselines3 in PyBullet.

The main objective of this project was to get an understanding of PyBullet and how the whole workflow of training a robot using reinforcement learning works.

Further updates will be to combine this with a text or voice prompt enabling the user to choose which cube to pick and place at a desired location.

Any further upgrades or inputs are highly appreciated from everyone here!

Checkout the code here: GitHub

DoorDash is reportedly in talks to acquire Instacart in a deal valued at approximately $17 billion.

The acquisition could increase the number of orders available for autonomous delivery robots by combining restaurant, grocery and convenience deliveries into a larger network.

One of the main challenges for delivery robots is utilization. Robots that complete only a few deliveries per day may struggle to offset equipment, maintenance and operating costs. Grocery and convenience orders could help increase utilization outside peak restaurant delivery hours.

We took apart a Zmotor linear actuator built for humanoid robots: stator, rotor and inverted planetary roller screw, all in a 32 mm body.

Video: https://youtu.be/7WQZlZuxGE8

Curious what people here think about IPRS vs. ball screws for humanoid joints.

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?

I’m a robotics software engineer exploring startup problems around deploying and operating commercial mobile robots, particularly cleaning, delivery, security and hospital logistics robots.

I’d like to hear from people who actually deploy, operate or support them.

Could you walk through a recent mission that needed human intervention or didn’t finish as expected?

A few things I’m interested in:

• What happened, and how did someone notice?
• Who handled it: on-site staff, your engineering team, a dealer or the manufacturer?
• Roughly how much human time, travel or rework did it involve? How often does something similar happen?
• How did you confirm the original job was eventually completed, including any work finished manually?
• What do your existing dashboards, remote support and operating procedures already handle well? What still takes manual coordination?

If initial setup and commissioning are a bigger challenge than ongoing operations, I’d be interested in that experience too.

If interventions are rare or already handled adequately, what makes your deployment work well? That would be equally useful to understand.

Please mention your application, role and approximate fleet size if you’re comfortable. An anonymised example is enough; no customer names or confidential logs needed. Hit me up in my DM guys.

Meet Nyro, a 3D-printed desktop companion robot with an expressive owl face, a moving head, and a few friendly hoots. 🦉

#robotics #embodiedai #ai #3dprinting

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

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

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!

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!

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.

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.

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.

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?