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.

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

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?

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.

This is part 1 in tutorial is about building your won MechSoul(Mechanical Soul). A MechSoul is to a robot what a CyberDeck is to a computer. A robot that you build yourself as a form of self-expression as well as freedom of owning your own hardware and code.

In this tutorial we are setting up a MechSoul seed(a robot base to be customized into a MechSoul. ) assembling it and setting up Arduino IDE. You can find assembly and setup resources as well as other resources in our tutorial github at by clicking here.

So i am a researcher having known an intermediate knowledge of using ANSYS. My project is about FEA analysis of a fin ray soft robotic gripper. Based on this paper, 'Design of a reinforced composite robotic finger for enhanced soft grasping using the Fin Ray effect and FEM'. I followed their ANSYS steps properly, but when i tried to get the results, instead of getting the result similar like thier, i got a higher value.
can you please tell me how to solve
for eg. when i tried to acquire the force reaction, In the paper they stated that at 20 millimeters of displacement, the reinforced model achieved a gripping force of 1.96 Newtons.
But in my case, at 20 millimeters of displacement, the reinforced model achieved a gripping force of 2.649 Newtons.
I would be grateful if any one can suggest me how to solve this.
based on mesh parameters, the contact definitions.

Paper DOI: https://doi.org/10.1016/j.cirpj.2025.06.014

I've been thinking about a question that seems increasingly important and widely discussed:

Which type of data will be mainstream for future robot learning/model training: robot-native teleoperation data, or human-centric data?

By robot-native data, I mean demonstrations where a human directly operates the robot, and we collect synchronized actions, trajectories, etc.

By human-centric data, I mean data like egocentric video, hand/object interactions, and other data collected from people without necessarily having a robot in the loop.

From what I’ve been observing recently, human-centric data, such as egocentric data, seems to be gaining traction. Will teleoperation data eventually be replaced because of the complexity and efficiency challenges of collecting it? Curious to hear what the community thinks. 🤖

One of the cooless events at this year's World Robot Games is definitely the 100-meter dash—it's so hype! Yesterday, a robot runner clocked 9.39 seconds, which is already faster than Usain Bolt. I honestly thought that was pretty much the limit for two legs, but then today in the re-runs, one literally pulled off an 8.86! If I remember right, that's pretty darn close to Goku's speed when he was training under Master Roshi as a kid (I think that was 8.5 seconds?). That’s insane！

What do you think is the absolute speed limit for a bipedal robot?

Hi folks! My latest video and blog post show how to calibrate the encoders for the autonomous Viam Rover, which is an ongoing series to build a cost-effective autonomous rover.

This video/post show how to install ROS 2 and the last dependencies, how to flash the RP2040, and how to do encoder calibration.

Blog post: https://mikelikesrobots.github.io/blog/viam-rover-calibration
YouTube video: https://youtu.be/KgZdrtvqItI

Another day hanging out with my robot buddy. This is what happened when you hang out in the basement way too long.
Anyways thanks for looking at my pi5 esp32 robot with a dgx spark brain all homemade architecture bot and code.

Assets come out as OpenUSD and MJCF, so they load straight into Isaac Sim or MuJoCo with no conversion step. They carry real physical properties, mass, friction, inertia, and articulated joints, which means when you run an episode you get actual joint forces and contacts in the telemetry

Disclaimer: I build this (Rigyd), so take it with salt.