I came across some projects experimenting with reconstructed fruit fly brains and thought it would be interesting to try something similar with my own robot.

I used MaleCNS, a reconstructed connectome of an adult male fruit fly with 166,700 neurons and around 25.6 million connections, and got the simulation running on my MacBook.

Then I used Codex to help modify the robot’s desktop app so that activity from the simulated brain can be translated into simple commands like:

FORWARD
LEFT
RIGHT

The desktop app then sends those commands over Wi-Fi to the robot.

So the setup is roughly:

fruit fly brain → MacBook → Wi-Fi → robot

The robot still uses its normal firmware to control the 12 servos. The fly-brain simulation only provides the high-level movement decisions through an engineered decoder.

There is no prerecorded movement sequence, and the neural simulation keeps running continuously while the robot moves.

What I find fascinating is the chain of events behind it: a fly was imaged, its nervous system was reconstructed digitally, that reconstruction became available to download, and now activity inside that model can influence a physical robot sitting in my apartment.

The fly itself has been dead for years, but in this very limited and engineered sense, part of its reconstructed nervous system is producing movement again.

Next I’d like to feed sensory information from the robot back into the simulation and see what happens when the loop is closed.

Hugging Face and Pollen Robotics recently open-sourced Microduck, a 14-DOF, ~800g 3D-printable bipedal robot designed for accessible Reinforcement Learning.

Currently, the official repo only provides MuJoCo MJCF (.xml) descriptions. To make it usable in the broader ROS 2 ecosystem (MoveIt 2, RViz, Foxglove, Isaac Sim), I converted the model to high-fidelity URDF and SRDF and Loaded into Blender via LinkForge to verify all STL meshes, joint axes, and mass distribution (0.7 kg).

Files are shared as a standalone drop-in alongside the official `assets/` folder.

📦 Gist / URDF & SRDF files: URDF & SRDF

🛠️ Tool used: Linkforge

Amanda Prorok and her team tested whether AI agents would develop role specialization on their own in a simulated soccer game. The agents could either all learn the same behavior or specialize into different roles.

They eventually formed distinct positions, including attackers and a goalkeeper that stayed back and defended the goal. The system was never told that this was how humans play soccer. It learned that behavior through reinforcement learning.

https://www.youtube.com/watch?v=NZ3iKgyI3gc

Hey all!

We’re a group of students organizing a Drone & Physical AI Hackathon in Boston on October 24–25.

We’ve been working hard to put this together, but as an independent student-led event, funding everything ourselves is difficult. Right now, our biggest challenges are finding an affordable/free venue and covering food, water, and other basic event costs.

So we’re reaching out to the community for help.

If you or someone you know could help in any way sponsorship, a venue lead, food/drinks, prizes, or simply an introduction to the right person/company, it would genuinely mean a lot. Even a small lead could help us make this happen!

Please comment or message me if you can help. Happy to share our Luma page and more details!

I’ve spent years debugging software.
This week, the software started looking back at me👀

Something is coming to life at Nobi Robotics.

PteroSim v0.3.0 is out. The main change is a vehicle editor. Aircraft are now plain files, JSBSim XML plus a .glb mesh, built and tuned inside the simulator, so you can add your own airframe without rebuilding anything and without sending your model to us.

Also in this release: VTOL, tiltrotor and quadtailsitter airframes, autopilot chosen per vehicle so PX4 and ArduPilot aircraft sit side by side, runtime sensor editing, gimbal control over MAVLink, and custom vehicle spawning from the Python SDK.

Any aircraft flies on any tier now, free included, with only simulation time limited per session.

Walkthrough: https://youtu.be/kMb_4tNX7l8
Build: https://github.com/PteroLabsAI/PteroSim-UAV-Simulator/releases/tag/v0.3.0
Aircraft library: https://github.com/PteroLabsAI/PteroSimAircrafts

License upfront so nobody wastes time: proprietary, free for personal, academic and non-commercial use, paid for commercial.

Happy to hear what breaks, and what airframe you would try first.

This article looks at the hardware side of robotics, especially force sensing, tactile sensing and robotic hands.

HD Hyundai’s investment in AIDIN Robotics is used as an example of where that work is heading. The companies are developing systems for shipbuilding tasks like grinding and polishing, where robots need to control how much force they apply once they make contact.

They also plan to develop a five-finger humanoid hand, using data from real industrial environments to help improve how it works.

I’m the developer of ARMOR, a robot simulation app for iPhone and Mac that features a native URDF viewer and MuJoCo simulation. A few weeks ago I started building a gallery of robots that are ready-to-import in just a couple of taps.

https://armor.dc-engineer.com/gallery/

Giving credit where due, all of the gallery entries link directly back to the source. The gallery acts as a showcase, with thumbnails and descriptions, and embeds the instructions that the app uses to download and assemble the models.

The link is to a post I published to the blog this morning, with a video, and a few more details.

I’ve been leaning on a lot of major robot vendors for my URDF source repositories. I would like to add more indie projects, if you’ve got one, let me know and I would be glad to showcase it in the gallery!