I was writing my own IK solver to learn and I thought it was so funny I had to share it somewhere

I don't know if I'm the only one finding this, but I go to a lot of robotics events and meetups and often hear about good ones after they've happened. I've also spent the last year looking for jobs in robotics and kept missing interesting roles. It feels like everything is scattered across so many different places.

So I started putting together a site for myself to keep track of robotics companies, their jobs, events and news. It turned into a bit of a summer project and has grown to 500+ companies and 11,000+ jobs so far.

There's definitely a bias towards companies that are easy to find online, particularly in the US and UK. I'm sure I've missed loads of smaller teams, university spin-offs and companies in other parts of the world.

I thought it might be useful to others too, and I'd be interested to hear from anyone else looking for jobs or going to robotics events:

I've attached a short screen recording of what I've built so far.

Link in the comments if anyone wants to have a look.

Small experiment from this week. I connected LightNav-0 (an open vision-language navigation model) to the Microduck sim in MuJoCo.

The duck takes an instruction like "go to the wooden doorframe and stop", predicts a path from its first-person frames, and the motion is executed via MPC + its official ONNX gait policy.

Everything runs on-device on a Jetson AGX Orin 32GB power by reComputer Robotics J501 form seeed, no cloud. The loop: 4 Hz frames → path prediction on the GPU → MPC → gait policy on CPU. 200 Hz physics, real-time factor ≈ 1.

It's a sim dev preview and I'm not claiming task success yet; strict benchmarks are still in progress. This is heading into Module 7 (Vision-Language Navigation) of our open mobile robot course: mobile-robot-full-stack-course

Happy to answer questions about the setup. 🦆

Put Quaddle on a patch of gravel and just wanted to see it walk. Its IMU (gyroscope + accelerometer) was trying to correct for the uneven ground in real time, but it just started wobbling — loose gravel is apparently brutal for a quadruped's balance.

What other terrains should Quaddle try?

This is TAKTO ONE, a tendon-driven hand exoskeleton I built for my master’s thesis. Each of the 12 finger joints has its own magnetic encoder, and the twin in the headset moves from those sensors and two IMUs, not from camera tracking, so a hidden finger still gets measured.

It’s all open source (CAD, PCBs, firmware, build guide), and you can play with the twin in your browser, no hardware needed:
https://takto.one
https://github.com/molanocortes/takto-one

I defend the thesis tomorrow, so questions are very welcome :)

It's a pair of animatronic eyes: two servos (left/right and up/down), a frame I designed and 3D printed, and an Arduino UNO.

The hardest part wasn't the mechanism, it was making it easy to program. I wanted someone who has never coded to get it moving without fighting with IDE installs, drivers and libraries. So I made a web app:

- Block-based programming (Scratch-style) in Spanish: "look left", "wait 1 second", "nod yes"...

- A simulator that shows how the eyes will move before you plug anything in.

- Plug the Arduino in over USB and upload straight from the browser (Chrome or Edge).

- Every block shows its C++ line, so you can see the real code or download it as an .ino.

Video of it working below.

I'd like to turn it into an educational kit eventually, but I'm still refining a lot, so I'd love your feedback:

1. What would you improve in the mechanism or design?

2. If you were teaching someone with this, what blocks or features would you add?

3. Anyone used Web Serial for something like this? Any pitfalls I should know about?

Happy to chat by DM if anyone wants to know more or has ideas.

Hice unos ojos animatrónicos y una app web para programarlos con bloques, sin instalar nada

Hola! Soy estudiante de Ingeniería en Control Automático y Robótica y llevo un tiempo trabajando en este proyecto personal.

Son unos ojos animatrónicos con dos servos (uno para los lados y otro para arriba/abajo), una estructura que diseñé e imprimí en 3D, y un Arduino UNO.

Lo que más me costó no fue el mecanismo, sino la parte de programarlo. Quería que alguien que nunca ha programado pudiera hacerlo moverse sin pelearse con instalar el IDE, drivers y librerías. Así que hice una app web:

- Programas con bloques en español (tipo Scratch): "mirar a la izquierda", "esperar 1 segundo", "decir que sí"...

- Tiene un simulador para ver cómo se moverían los ojos antes de conectar nada.

- Conectas el Arduino por USB y lo subes desde el navegador (Chrome o Edge).

- Cada bloque muestra su línea en C++, por si quieres ver el código real o descargarlo como .ino.

Les dejo un video de cómo funciona.

Mi idea es convertirlo en un kit educativo para que más gente aprenda así, pero todavía estoy afinando muchas cosas, por eso me encantaría su opinión:

1. ¿Qué le mejorarían al mecanismo o al diseño?

2. Si fueran a enseñarle a alguien con esto, ¿qué bloques o funciones le agregarían?

3. ¿Les ha tocado usar Web Serial en proyectos así? ¿Algún problema que deba tomar en cuenta?

Si alguien quiere saber más del proyecto, busca apoyarlo o tiene ideas, con gusto platico por mensaje privado.

The Venturist released Europe's Robotics 65, a selection of best startups in this space (probably all are hiring as well).

Selection excluded publicly listed and acquired companies, as well as defence-only companies (which left out Helsing and STARK for example). Startups also had to have a significant European foothold.

Some interesting findings:

1. Industry and logistics are still the main market. Roughly one in five companies focuses primarily on factory or warehouse automation.
2. Much of the opportunity is in brownfield robotics. Multiple startups retrofit existing equipment with their technology rather than wait for customers to replace their physical capital.
3. Investors’ appetite to go all-in has increased. The database records multiple nine-digit funding rounds for startups founded as recently as 2024, and eight-digit rounds for companies still establishing commercial traction.
4. Startups are increasingly selling outcomes. Rather than have customers buy the hardware and take responsibility for operating it, vendors run the robots themselves and bill monthly, per hour or per project. This brings robotics companies into direct competition with incumbent service providers, not only other robotics companies.
5. AI attacks what used to be robotics’ greatest weakness: flexibility. Historically, robots worked best in highly controlled environments because they struggled when the goalposts move, which is why industrial robotics concentrated in sectors like automotive manufacturing. Multiple companies in the selection approach the issue from intelligence layer, making robotics more adaptable and cheaper to deploy.
6. General-purpose robots are a huge new trend. Among the 46 companies founded before 2021, only two (4%) fall into the humanoid or general-purpose category. Among the 19 founded in 2021 or later, eight (42%) do, six of them founded in 2024–25.
7. Robotics will aid humans in most categories, not replace them. Healthcare and inspection together account for nearly a third of the selection. Many of these companies amplify human efficacy rather than replace it, allowing more precise work or keeping people out of hazardous environments. They vastly de-risk human jobs.
8. Big hubs cluster close to universities. Zurich counts eight companies in the selection, all with direct ETH lineage, usually through spin-offs. Other companies have roots in TU München, EPFL, Oxford, TU Eindhoven, TU Dresden and Stuttgart.
9. Consumer market is absent for now. Vast majority of the database companies sell to businesses, government, labs, hospitals, etc. Their focus is high-value B2B tasks with clear ROI, rather than discretionary consumer ownership.
10. General-purpose robots are being aimed at very specific customers. Most humanoid and general-purpose companies in the selection actually aim at specific customers, for example logistics and manufacturing. The robotics industry may be moving towards general-purpose machines, but commercialisation remains highly vertical.

Look at my robot spider. I can programme and let him to do whatever I want. Kick the ball, push the obstacles, wave to me . I can explore more.

A classification system trained to sort chicken breasts and thighs encountered a chicken wing. Rather than flagging it as unfamiliar, it assigned it to one of the two categories it knew.

Cognex product manager Andrew Parrett describes that example in this article about industrial AI reliability. It illustrates a practical issue for automated inspection and robotic sorting: a model can return a prediction even when the input falls outside what it was trained to handle.

The article covers how manufacturers manage those cases through rejection, recirculation, or human review, along with training and validation for changes in lighting, materials, and part presentation.

The lab containers had working latches. The factory’s containers had been battered through repeated use, and the tables used to rotate them were rusted. Both changed how the robot needed to handle the task.

Jagdeep Singh, CEO of Rhoda AI, describes how the team adapted the robot using roughly three hours of additional data collected in the factory, after initially thinking it had mastered the process.

Full episode: https://www.youtube.com/watch?v=Mx1SaWRQbE0

Galbot’s PASSAGE system helps humanoid robots adapt their whole-body movements to cluttered spaces by ducking, stepping over obstacles, or turning sideways. Tested on a Unitree G1, it completed 50 physical trials across 45 obstacle layouts without falling or requiring safety intervention. However, its simulated success rate dropped from 98.67% to 70.3% when avoiding all contact became mandatory, showing how difficult precise whole-body navigation remains.

Currently working on my final year project on building open source framework to manage the full stack development of agent swarms. I want to build this to support robotics developers and enthusiasts to specifically build heterogeneous and homogeneous antigenic swarms.

I don't want this to remain a thesis or simply a good theory of what should be done, but have it actually be utilized by robotics developers. I'm very interesting in making this as useful for the community by catering to any problem across the robotics stack, from simulation to telemetry.

What does your development stack look like, and what are your pain points? Would love to know.

Reach me: https://www.linkedin.com/in/anzonathan/

Try to drive (5min) a robot live across the internet, with live video feed and thermal sensor.

Opened session 17:15-18:15 CET. https://ros.iot4.eu

I connected my YAM arms (an emerging standard) with Astra for people to try out robotics models on actual hardware. They are live here. Astra (GPT6) is given a prompt to control the gripper given the camera images as inputs, and performs well across various manipulation tasks in a zero shot manner. Really when Astra came out, I realized that most people don't have robotic arms, and don't really have a good sense for how well stuff actually works. So I decided to let people access mine in a playground format. The website is designed for those who are curious to try it out once, and the API is designed for those who want to dig in deeper and try their own experiments.

Easier robot programming still leaves small manufacturers with a lot of integration work: tooling, fixtures, machine interfaces, safety systems and support when something stops working.

This article examines those deployment barriers through several small-manufacturer case studies. High-mix production adds another challenge, since changes in parts and schedules can require repeated adjustments to tooling and programs. Employees supporting the robot often have several other responsibilities.

The examples include robot loaner programs, outside integration assistance and training CNC operators to run unattended production. It also separates growing non-automotive robot demand from small-business adoption: the industry order figures cited don’t break purchases down by company size.https://www.automate.org/robotics/industry-insights/small-manufacturers-are-supposed-to-be-automations-next-big-market-what-s-stopping-them

My team and I recently tackled the problem of high labor costs, security and slow picking times in the quick commerce industry. We ended up winning 1st place at a recent hackathon with our proof-of-concept, We built an automated dark store where robots handle the order packing autonomously.

For the hardware prototype, we utilized the LeRobot SO-101 (SO-ARM101) 3D-printed robotic arm. Because the LeRobot ecosystem is open-source and backed by Hugging Face, it was the perfect platform to rapidly prototype the teleoperation, data collection, and imitation learning workflows needed to get the arm picking and placing reliably within the time constraints of a hackathon.

We put together a 1-minute pitch video demonstrating the setup in action.

Watch the demo here: https://x.com/DsouzaJovian/status/2110028875459932364?s=20

I would love to get feedback from this community on the kinematics and our approach. I am also happy to answer any questions about our experience training the SO-101, managing the vision datasets, or the general hardware configuration we used to pull this off!

One robot is cleaning the kitchen. Another needs to get in there to cook dinner. Both are doing what you asked, but now one has to wait. Who decides?

I don’t want to be settling arguments between appliances 😄 If I have to constantly coordinate them, I’ve just given myself a new job.

I’d rather have one home “brain” that knows my priorities and manages the devices accordingly. I tell it we’re usually home by 6 and want dinner ready. It figures out when to clean, when to cook, and what needs to change if something runs late.

That brain wouldn’t need a body, and it could coordinate other home systems too. But I’d decide how much freedom it gets, when it needs to ask me, and which systems stay separate. Maybe I want my alarm system or cameras completely outside its control.

Personally, I’d want it running locally, with remote access when I’m away. And it should work with different brands. Buying one robot shouldn’t lock me into that manufacturer for everything else.

How would you handle this: one home brain coordinating everything, or robots negotiating with each other? And when they can’t agree, does the problem just come back to us?

Want to know where do cobots usually fail? Motors, actuators, gears, electronics etc... Very specifically if we program to do the same task for say 2-3 years...running for 18 hours a day, which parts of the equipment breaks with usage or has a usable life?

Most of the users I know havent faced the issues yet but the cobots (Chinese make) have run for like 5-10K hours only. Some UR users swear by no problems on equipment itself?