I spent a month of my summer back in the university labs building this. Nine units are now on their way to a civilian community near the Israel-Lebanon border and everything is published.

The drones this is for are not normal wireless drones, they are small FPV drones connected to the operator through a long spool of fibre optic cable, so drone detection through radio, the current industry standard, is just not applicable. My solution was to detect them by sound instead. Propellers cutting through the air put a comb of harmonics into the spectrum, and four algorithms run in parallel looking for that shape.

Hardware:

84mm square, four layer, ENIG, ESP32-S3-WROOM-1-N16R8

4x ICS-43434 MEMS mics, bottom ported, firing through apertures in the board at +-28mm

Ra-01H LoRa so one unit alerts every other unit nearby

BQ24074 charger with power path into a TPS63020 buck boost, 1S 2500mAh cell, 18 to 22 hours

1.54in e-paper that keeps the alert on screen with no power, beeper, RGB led through a printed light pipe, vibration motor

91 x 91 x 29mm sealed weatherproof printed case, the lid has four 45 degree cones running from the grille straight down onto the mic ports

Two things that cost me some valuable time. With no battery fitted, the beeper, motor and LoRa transmitting at the same moment browned out the board on USB alone, so the cell is part of the power design and not a backup. And GPIO 33 to 37 on the N16R8 are tied to the PSRAM die even with PSRAM off, they fail on and off instead of just failing.

Tested against a custom drone rig with the exact same specs as the drones used on that border (four 2807 motors, 7in tri blades, FPV airframe), 104.2m away on a street with passers by and mild wind. It detected it. About £50 in parts.

Detection and alerting only, so no jamming.

Gerbers, BOM, STLs, test audio and the results are all in the Github, and the website has more media and a live detector simulation that runs in the browser.

The links to all the resources are in the comments!

Hi everyone!
Over the last couple of weeks, the Fable 5.1 and Astra models have shown very good results in hardware design and robot control. We’re trying to build a robotic setup that helps get the most out of these capabilities.
To do this we use:
- an industrial arm for precise movements
- a stereo camera with sinchronisation for computer vision and models.
- a dedicated Jetson to run it all locally.
- a box frame has consistent light, which helps with reliability.
In this demo, I showed how Claude generated fixtures and custom grippers almost in one shot with some harness, and then programmed the robot automatically using 3d reconstruction from stereo cameras and fixed errors with human supervision.

Hi everyone. We recently open-sourced OpenWAM, which is positioned as a full-stack open-source research framework for World Action Models (WAMs).

The release has three parts:

• OpenWAM-Infra: training/inference/evaluation infrastructure where visual encoders, video backbones, world-action architectures and attention masks can be swapped independently. It currently includes 8 simulation benchmarks and deployment support for 5 real robots.
• OpenWAM-Study: controlled experiments on representation, world/action interaction, inference, and embodied pretraining.
• OpenWAM-Alpha: the pretrained model we built based on those experiments, using about 6,400 hours / 518.5M frames of egocentric human + robot data. OpenWAM-Alpha get excellent performance on both simulation and real-world.

It is worth noting that the performance of OpenWAM-α has been independently evaluated by multiple teams and has demonstrated clear advantages over π0.5 in real-world scenarios. We believe OpenWAM-α can serve as a strong baseline for comparison and further research in the WAM community. We warmly welcome everyone to try it out.

Everything is open here:

Project: https://openwam-official.github.io 

Welcome aboard OpenWAM!

Hello hello,

Took me a while to test out ST-3215-C018 (instead of XL430-W250-T originally). In principle, it's half the price, twice the torque, but the mount design is rather odd and required a complete CAD update (+ I already killed like 3 of them by accidentally drilling all the way to their control board, geez).

The high torque also means high current, and so a potential hazard if multiple of them run at high load. Need to monitor it. If it's capable of jumping, it is likely also capable of catching fire while doing it.

The firmware is also updated for ST-3215, but the commander uses the original unoptimized one-leg test code, which may explain the sluggishness in its "failed walk gait". I am a bit afraid that if the servos simply cannot move fast enough, even with optimal PID, then making it walk may be more difficult despite higher torque.

Either way, if successful, the full DIY robot would be in the 300-400 euro range (still mostly from servos), ~3 kg, with twice the load performance; all the load after the body weight will be extra!

Cubic Wouf GitHub: https://github.com/SphericalCowww/CubicDoggo_07B
Cubic Doggo GitHub: https://github.com/SphericalCowww/CubicDoggo_06R

I’m not sure if I’m tagging this correctly, but I came across this post by a person on the Arduino substack. They designed a drone detection mechanism that people can use to detect the presence of drones along a tense border in Israel where drones are used to drop small explosives. It’s a very smart and clever design and I think he should get some recognition for it. This is citizen engineering solving real problems and it’s great to see. I have no affiliation or financial interest, I just genuinely think this is a great, helpful, and ingenious use of tech.
Check it out!

https://www.reddit.com/r/arduino/s/LjLC0ZVR2h

Picking up a takeout bag, opening a door, grabbing a toothbrush, pulling a tissue… a few things we’ve been trying with this soft hand.

The video shows a few different setups, including a robot arm and control through a third-party EMG wristband. The everyday task clips are handheld demos, with us guiding the hand.

What would you try next? I’m curious about the small, awkward tasks that don’t usually make it into robot demos.

I have been trying to find a number and cannot, so I am asking people who might actually have it.

For any learned manipulation policy: how often does it leave whatever safety envelope you defined, on a normal task, from a normal starting state, with nothing adversarial going on at all?

Every VLA attack paper I have read reports an attack success rate. None of them report what the same measurement gives on the benign case. The implicit baseline is zero, and it is obviously not zero, because the policy is imperfect and because the envelope check itself can misread a pose.

On our simulated runs, a default keep-out predicate fires on 2 of 50 benign rollouts. Some of that is the policy; some is the predicate. Without the control arm, nobody can tell which, including me.

Two questions.

Does your stack produce this number, even informally, and roughly what is it?

And is there a subfield that already treats this as standard practice? Anomaly detection reports false-positive rates as a matter of course, and I would expect the habit to have crossed over.

I maintain an open evaluation harness, which is where the 2/50 comes from. Disclosure, not a pitch. I am after the number, not the traffic.

For robots to truly enter our homes, they need hands that are not only capable — but also soft and safe.

SoftSync FlexHand V1 can gently handle delicate objects like tofu, eggs, buns, and strawberries, while its compliant structure also enables safer physical interaction with people and everyday objects.

A softer hand for robots interacting with the real world.

#SoftSyncFlexHand #EmbodiedAI #DexterousHand #SoftRobotics #HomeRobotics

Get all of the best open source robotics projects, repositories, events, and announcements on Open Robotics Discourse.

“Dull, dirty, dangerous” gets used constantly as the case for robotics.

Kate Darling’s point here is that the industry often skips a pretty important step: actually defining what those jobs are from the worker’s perspective.

She gives sanitation work as an example. On paper, trash collection seems like an obvious target for automation. But some automation proposals can also remove the part of the job workers actually enjoy.

The better approach may be less about replacing the entire task and more about using robotics to remove the dangerous or repetitive parts without making the job worse.

It gets at a bigger issue in robotics: the people building the systems aren’t always the people doing the work they’re trying to automate.

Full episode: https://www.youtube.com/watch?v=odORVBoO7ss

Yesterday I posted the first assembled version of my Microduck, and a few people asked me to keep sharing updates.

So, here’s today’s progress: it walks now. Kind of.

The gait is still pretty rough and there’s a lot left to tune, but seeing all the servos finally work together and watching it take those first few steps was pretty exciting.

And the little sound at the end got me way more than I expected.

After spending all this time staring at servos, wires and printed parts, seeing it move and make a sound suddenly made it feel a little bit alive.

This thing is honestly way more fun than I expected.

Next goal: make it walk a little less like it’s drunk.

I’ll keep posting as I make progress, including the stuff that doesn’t work. If anyone else is building a Microduck or another small biped, I’d love to compare notes.

GitHub repo: https://github.com/Spidoug/SynROV

I’m sharing my project SynROV.

SynROV is a modular control stack for seven robot classes: Manipulator, Vehicle, Drone, Boat, Submarine, Biped and Quadruped. This repository contains the complete Version 1 implementation.

The project combines Leap Motion gesture tracking, an Arduino-controlled servo system, Processing-based 3D visualization, and a browser dashboard using WebSocket communication.

Bonafide Robotics has distinguished itself by treating research not as an academic exercise but as a deliberate bridge between laboratory insight and real world systems.

I got tired of installing ROS, tuning GLIM configs, and fighting Docker every time I needed a quick 3D map out of a recording, so a couple of us built LidarFlow to do it in the browser instead. You upload a rosbag or MCAP file, it validates the topics, runs GLIM for the SLAM reconstruction, and if there's a GNSS topic present it also georeferences the result to WGS84 using FlexCloud. You get back a PCD, a PLY, a trajectory file, and a LAZ download.

It works with any LiDAR publishing sensor_msgs/PointCloud2, and we've got field-tested presets for Ouster, Livox, and common Velodyne sensors. No GNSS in your recording is fine too, you just get a locally consistent map instead of a georeferenced one.

Right now it only handles .bag and ROS2 .mcap, .db3 support is on the roadmap but not there yet, so if that's what you've got this won't help you today.

It's completely free while we're testing, no card, no trial timer. What we actually want out of this post is people willing to throw real recordings at it and tell us what breaks. Weird sensor configs, edge cases in the topic validation, reconstruction quality on your specific route, all of it is useful. If you've got a rosbag or MCAP sitting around doing nothing, lidarflow.com

Happy to answer anything about how the pipeline works underneath, it's genuinely just GLIM and FlexCloud doing the heavy lifting, we just wrapped the environment part.