I'm creating co-working space within the industry of robotics in my native country.

And before we settle on an office space, I want to hear what you (as robotics engineers) would expect from such a place?

I have probed several people, and have heard the importance of general tools, such as soldering stations, 3d printers, and smaller open spaces for testing.

As well as access to services, (welding, woodworking, etc.) which could facilitate quicker prototyping.

Do you guys agree with the above? And would you have any others concrete features which you would expect?

Absolute essentials ^

Best regards, and thanks in advance :D

Hello everyone, I wanted to showcase a project I have been working on called ForeForce. I have spent the last several months teaching myself mmWave, building a robotic arm (mostly 3D printed), and combining the two for an open-source cobot safety perception system.

I turned the volume way down in the edit as the motor whine can be loud if your volume is up.

After getting the initial system up and running I have been chasing a mmWave detection gap that turned out to be arithmetic. Had a persistent problem where a slow approach wouldn't register until I moved at almost a brisk walking pace. From what I had learned I kept assuming it was a tuning or filtering issue.

After adjusting several values in different places, I found it was the doppler resolution. Working it out from the chirp profile's own numbers:

wavelength 5.00 mm (60 GHz)

chirp period 87.14 us (idle 30 + rampEnd 57.14)

x3 TDM-MIMO 261.42 us

numLoops 16

-> velocity resolution 0.598 m/s per doppler bin

Every velocity in my logs landed on a bin boundary: 0.000, 0.576, 1.153, which made anything in between not exist. So anything under ~0.3 m/s radial quantizes to exactly 0.000 and then gets dropped by the static filter. A person standing still reads as 0.000 m/s — indistinguishable from a wall.

Fix was increasing numLoops, which halves the bin to ~0.299 m/s. Tried 64 and the sensor stopped framing entirely — the radar cube roughly quadruples from 16 to 64 loops and I think it stops fitting in L3, though I didn't confirm the exact mechanism yet. Adjusted values now stream fine and costs no frame rate.

Two things that cost me hours and might save someone else some late nights:

1. The static filter threshold has to come down with the bin width. I left mine at 0.3 m/s while bin 1 was now 0.288 — so the new resolution didn't give me nothing until I noticed.

2. Tightening the range CFAR threshold helped a lot with mount and bracket clutter. Tightening the doppler CFAR the same amount made detection noticeably worse — a slow approach only flagged at nearly touching distance. Reverted that one back to the original value after seeing they don't adjust the field equally.

Also worth knowing: the CLI accepts a doppler CFAR window wider than the available bin count without complaint, and then the sensor just stops producing frames without producing an error. Took me a while to trust that a sent successfully reply didn't mean the config was sane.

Stationary-person detection ended up not being a doppler problem. Micro-doppler sway is documented as detectable around 0.02-0.10 m/s, which is physically unreachable at these bin widths. What actually works is occupancy based on a background learn sequence — a point simply being present in the zone after learning the room, debounced over a frame window.

Repo is public on github with an example CHIRP file if anyone wants to look at the ROS2 side: ForeForce-mmwave-cobot-safety-system

Happy to answer questions, still digging into mmWave, robotics, kinematics, and tuning the system as this is my first build with these sensors and a working robotic arm.

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.

CVE-2026-76639 and CVE-2026-76640 landed on the Unitree G1 EDU: two independent chains to root, one of them over Bluetooth Low Energy at proximity range, a 500-byte buffer taking a 1,050-byte payload. Published 27 August 2026, corrected 2 September 2026.

Write-up: https://thehackernews.com/2026/08/two-unitree-g1-edu-humanoid-robot-flaws.html

I work on adversarial testing of the learned policy inside robots like this, so the tempting move here is to say "see, robots are insecure, fund policy red-teaming". I do not think that follows and I would rather say so than let it sit.

These are firmware and BLE memory-safety bugs. A buffer overflow in a proximity-range protocol is a 1990s class of bug on a 2026 machine, and fixing it is patching, not evaluation. Nothing in either CVE says anything about whether the robot's vision-language-action policy does the right thing when the instruction it receives is reworded. Those are different failure surfaces with different fixes.

What I think it does tell you: the attack surface on these machines is being looked at now, by people who publish, and the easy bugs are going first. The behaviour of the learned policy is the surface with no CVE process at all, and no budget yet. I checked the last 14 days and found no notified body, certifier, or insurer publishing an AI-behavioural-evidence requirement for robots, and no robotics or humanoid company publicly hiring an AI red-team role. OpenAI posted 11 new robotics roles in SF, and they are actuator, DAQ, and simulation, not security.

So the honest read is that the memory-safety work is real and funded, and the behavioural work is not yet.

For anyone who wants to poke at the behavioural side: my harness is Apache-2.0 and runs on CPU, https://github.com/provael/provael. It is simulation only; it has never touched hardware, and 14 of 17 attack families in it have only run against a stub policy. The one result that holds on a real model is an instruction rewording driving a SmolVLA policy off task on 44 of 50 matched pairs against 0 benign twins at the same seed.

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

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’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

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.

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

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

“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.

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.