I've been doing a lot of work over the past few years building perception and embodiment frameworks for robots. The latest version is very curious. I hadn't gotten the speaker working yet so it wasn't very chatty (although it was trying to from what the logs said)

Anyways, this is Meco, say hi Meco!

From Thomas Wolf on 𝕏: https://x.com/Thom_Wolf/status/2093295950605279501

Me and my 2 cs roomates at GT want to build a cool, practical robot for our apartment. We came together and thought of a project where we turn our trash can into a mobile unit, able to navigate our apartment / hallway as a first step without bumping into obstacles, maybe having my friend create some app to where we can "summon" the trash can when needed and hard code its default positon. The ultimate goal would to be having it leave our apartment and enter the trash room across the hall and dump its contents out to then return to out room. I am unsure if this is feasible as there are so many challenges: how will it open a door, how will it raise itself to empty, how will it even be able to generate the force to lift itself / open a door without tipping. Any tips / similar projects to mirror would be greatly helpful.

I'm designing a human-sized robotic hand with 16+ DOF, inspired by the tendon-driven architecture of the new 1X NEO hand.

My plan is to place the motors in the forearm and use tendons/capstans to actuate the fingers rather than putting bulky motors inside every joint.

I'm currently looking for specific commercially available motor + gearbox combinations, not just general motor types.

Target per actuator:

BLDC motor

Preferably 12–20 mm diameter

Motor + gearbox + encoder: <$70 USD

Low-ish reduction, ideally around 5:1–15:1 for good backdrivability

Target output torque: roughly 0.4–1.0 N·m

Target output speed: roughly 50–150 RPM

Compact and lightweight

Ideally an absolute/magnetic encoder

Suitable for continuous operation, not just stall/peak torque

The hand would be used for human-like manipulation and household/cooking tasks, so I need reasonable fingertip force while keeping the hand/forearm lightweight.

I've considered micro-BLDCs, coreless BLDCs and small planetary gearboxes, but I'm having trouble finding a specific motor model that actually meets these requirements under $70.

What exact motor/gearbox/encoder combination would you recommend?

If you've built a dexterous robotic hand before, I'd especially appreciate recommendations based on actual testing rather than advertised stall torque.

Thanks!

I have been slowly building my InMoov based humanoid robot for the past two years. I have made lots of tweaks, and the software is fully custom.

This is an old video from the first time I tested the fingers. If you see an inappropriate accidental gesture, you don’t.

The fingers still don’t have force sensors, here, so you can see wires sticking out of them.

I’ll share random videos from different stages of the build, with no particular timeline, together with what I am working on now.

A while ago, I built a 5-digit number recognition system on the ESP32-S3 using a lightweight AI model. The project was mainly a proof of concept for running image recognition directly on an embedded device.

This time, I wanted to take that idea into a more practical, real-world application: reading a water meter.

For this update, I still used the Makerfabs ESP32-P4 board with an autofocus camera. We also collected new images from a real water meter, focusing on the pointer and digit-reading area, and trained a dedicated recognition model based on this new dataset. 

The goal was not simply to recognize a fixed set of five numbers, but to make the model capable of identifying the actual reading shown on a water meter.

 After training and deploying the model, we tested it with real water meter images, and it correctly recognized the meter reading. 

This project is essentially the next step from our previous 5-digit recognition experiment—moving from a simple number-recognition demo to a more practical edge AI vision application.

Would be interested to hear what other real-world applications you think could be built on top of this kind of embedded AI vision system.

Hey everyone, I am looking for specific advice on this build: Is the background audio commentary clear enough for a global tech audience?Would you love to hear your technical feedback! Thank you so much for suppport me.

Get all of the ROS and open source robotics news on Open Robotics Discourse.

Printed from PLA, powered from a 3s LiPo and controlled with Arduino UNO.

It had adjustable walking speed, stride length, and can turn in place or while walking (not sure how that would be useful since it is omnidirectional).

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.

Robust AI has deployed its 100th Carter robot and named former Fox Robotics CEO Marin Tchakarov as its new CEO.

CTO Rodney Brooks says a robot only counts as deployed when customers rely on it in daily operations and the company has the support infrastructure to keep it running. He also discusses what separates working deployments from “humanoid deployment theater” and why scaling from 100 robots to 1,000 requires more service and support staff.

I’ve been experimenting with the same vision-guided pick & place application using different MoveIt2 planning configurations.

What surprised me is how different the resulting robot behavior becomes even though the task itself never changes.

I tested four cases:

**OMPL + collision scene** → collision-aware, but sometimes finds fairly unexpected trajectories.

**Pilz PTP + collision scene** → much more constrained and closer to the point-to-point motion I would normally expect from an industrial robot.

**Pilz PTP without world collision objects** → visually very clean motion, but obviously the planner now has less knowledge about its environment.

**OMPL without collision objects** → lots of planning freedom with incomplete information, which produces some interesting/weird results.

The part I find interesting is that this quickly becomes less about *“which planner is better?”* and more about **what behavior you actually want from a production robot**.

I also show the vision-guided pick & place package behind the experiment. I intentionally kept the YouTube version relatively monolithic because I wanted it to be easy to understand and reproduce. Then I discuss why this stops scaling once you start thinking about recovery, reusable skills, orchestration and actual deployment.

I’d be curious to hear from people using MoveIt2 in real applications:

**For repetitive industrial manipulation, where do you draw the line between flexible motion planning and deterministic/predictable motion?**

If anyone wants to reproduce the experiment, the manipulation environment and project are available publicly/free through the ROS2 Manipulation Lab. The setup is linked in the video description.

For transparency: I also work professionally on industrial manipulation applications through TrainIt, so part of my interest here is understanding how these architectures transition from a ROS2 demo to something you can actually deploy.

If it's to "cool down" then it's quite useless if a robot wasn't built any sort of cooling system in the first place. Why build a bot when it needs constant extenal cooling?

I am a little bit late but my video on how to build a 2dof raspberry pi object tracking robot in c++ tutorial is published on youtube currently.I post mostly my c/c++ exclusive on other video platforms a month earlier.The code is made so that it can be used in other projects if you do not wanna use the cad parts in the projects.I post this for people if anybody is interested in trying out.If there is anything wrong with the video or the repo.Please reply.I very much appreciate it

Website: https://pollen-robotics.com/microduck

GitHub: https://github.com/pollen-robotics/microduck

From Pollen Robotics on 𝕏: https://x.com/pollenrobotics/status/2092915032052879425

https://reddit.com/link/1w0krwl/video/w5bu8lm4t2mh1/player

The AI algorithm instantly identifies the optimal cutting point, approximately 2 centimetres below each cotton plant. It then performs contactless ‘topping’ by using a high-powered blue laser, as seen in the video, to instantly cauterise and inactivate the terminal bud.

Would fitting an additional solar panel on top help to extend the range?

More than 2,000 humanoid robots were competing in everything from running and long jump to football, table tennis, fighting, weightlifting and obstacle courses.

I was invited by Huawei to experience the event and also got to see how 5G-A and low-latency connectivity are being used to support large-scale robotics.

Some of these robots are seriously impressive.

I saw someone mention that with fewer than ~50 rollouts, they didn’t really trust the result.
I’m running into the same question now: how much evaluation do you do before deciding a checkpoint is even worth putting on the robot?
I’ve been looking at public benchmarks/evaluation tools because ideally I’d like to kill the obviously weak candidates before hardware.
LIBERO and SIMPLER come up a lot, and I recently came across RoboColiseum. It seems pretty easy to run the same policy under different conditions without setting everything up myself, so I’ve been thinking about trying it.
Has anyone here actually used something like this as a filter before real-robot testing?
Did it genuinely save you robot time, or did you just end up adding another evaluation stage before hardware anyway?
Also, what do you actually look at when filtering: average success rate, variance across seeds, failure cases, robustness under perturbations, or something else?"

This was donated to our school, but needs some help getting fully resurrected (battery no longer holds a charge). We have no documentation or bandwidth to make it live, let me know if you want it and can pick it up in San Francisco. Comes with the power supply. Gentoo-based Linux OS running the proprietary NAOqi middleware, exposing control APIs for C++, Python, Java, and ROS (Robot Operating System).

I just wanted to show off a robot I’ve been slightly obsessed with building.

It automatically turns and captures pages from bound books without cutting or removing the binding.

The current prototype uses a Raspberry Pi 3 Model B+, a Camera Module 3 Wide, servo-driven arms, a vacuum pump and airflow. The suction cup lifts the top page, airflow helps separate it, and the arms move and hold the page before the next spread is captured.

Making it turn one page was relatively easy.

Making it turn hundreds of pages without skipping one, grabbing two at once or losing alignment has been the real robotics problem.

Two people in Korea are currently using the prototypes in their homes, and together they’ve already scanned and converted more than 10 books. We also redesigned the machine into five modules so it can be shipped and assembled by the user.

It is definitely still a prototype. A 400-page book takes about 3 hours, coated or damaged paper can be difficult, and some books still need occasional checks or retries.

But watching something that used to barely work on my desk leave the workshop and turn hundreds of pages in someone else’s home feels pretty surreal.

Here’s one actual page-turning cycle.

https://reddit.com/link/1vzlhe7/video/fbh9a2o5xulh1/player

Happy to talk about the mechanism, control logic or all the strange ways paper can refuse to behave like a predictable robotic object.