Italy has some of Europe’s best robotics talent. So what’s stopping it from building the next generation of global robotics companies? That’s one of the questions we brought to Turin yesterday. Over the past year, I’ve met quite a few Italian robotics founders and engineers. When I heard about Italian Tech Week, now Wave by @vento_ventures, I saw an opportunity to bring some of them together. Italy has an extraordinary industrial heritage, world-class engineering talent, and a strong robotics research community. So yesterday, @22Astronauts_ brought together founders, investors, researchers, and industry leaders at Comau’s headquarters and manufacturing facility. I couldn’t have imagined a better place. Part of our “Not Another Panel” series, we discussed why robotics pilots struggle to reach production, who should take the risk of first deployments, and how manufacturers can become more involved in the startup ecosystem. A big thank you to Francesco Ricciuti from Visionaries Tomorrow and Mattia Marconi from @ABBRobotics for shaping the conversation. And especially to Giuseppe Costabile, Ilaria Polinetti, Davide Cavallini, Stefania Ferrero, Gioacchino Civiletti, Irene Sabbetti, and the entire Comau team for being such generous hosts. After New York, Paris, and San Francisco, I hope Turin is the beginning of many more conversations that turn into collaborations, first customers, and robots working on factory floors. What do you think Italy needs to produce more globally successful robotics companies?

Wait. Am I getting this right? These guys just came out of SIX YEARS in stealth with a machine that takes code and spits out actual working micro-machines??? Tiny devices with moving parts, without the usual per-product tooling or months of process development. A different code… BAM: different machine! A regular factory locks in custom fixtures and process recipes for each thing it builds. 3D printer…? Just changes the shape. What Atomic Machines describes is closer to a compiler for physical devices: The system measures the result after every step, feeds that back, and only lets the part move forward if it is in spec. Their first output is the PrimeSwitch PS-150, a hermetic relay about the size of a fingernail that carries 150 A continuous, opens to 1,500 V isolation in 50 microseconds, and draws no power while holding the state. Aimed at the 800 V DC rails showing up in AI data centers, where mechanical contactors are too slow and solid-state switches bleed heat and cannot fully isolate. If you are even remotely interested in manufacturing, you know this a different c a t e g o r y of manufacturing. Congrats to @jeffholden and Klaus Zietlow and the rest of the team!

Plenty of cool things going on, but I’d really like to meet a few people deep in manufacturing, automation, and robotics. If that’s you, here at Wave by @vento_ventures. come say hi.

Wait. Am I getting this right? These guys just came out of SIX YEARS in stealth with a machine that takes code and spits out actual working micro-machines??? Tiny devices with moving parts, without the usual per-product tooling or months of process development. A different code… BAM: different machine! A regular factory locks in custom fixtures and process recipes for each thing it builds. 3D printer…? Just changes the shape. What Atomic Machines describes is closer to a compiler for physical devices: The system measures the result after every step, feeds that back, and only lets the part move forward if it is in spec. Their first output is the PrimeSwitch PS-150, a hermetic relay about the size of a fingernail that carries 150 A continuous, opens to 1,500 V isolation in 50 microseconds, and draws no power while holding the state. Aimed at the 800 V DC rails showing up in AI data centers, where mechanical contactors are too slow and solid-state switches bleed heat and cannot fully isolate. If you are even remotely interested in manufacturing, you know this a different c a t e g o r y of manufacturing. Congrats to @jeffholden and Klaus Zietlow and the rest of the team!

Deepak Pathak @deepakpathak (Co-founder & CEO, Skild AI) joins the Humanoid Hub Conference as a keynote speaker. One general-purpose brain, many robot bodies. He'll be unpacking how @SkildAI is getting there. Oct 15-16, SF

It beats other methods and matches the best existing system! 📌 To create better data for robots, 3D maps, and simulations of real places. A new system that tracks where a person is and builds a 3D map from a head-mounted camera. GroundedSI released GroundedSLAM. It stays accurate even on long walks through rooms and buildings. It combines cameras and motion sensors, corrects small errors while running, and cleans up the full path afterward. RoboCap users can try it now through the Grounded API. Credit: Grounded Superintelligence (GSI / @GroundedSI), with BitRobot 📌 Paper: Blog: —— Interested in robotics and AI insights? Subscribe free:

Forget lidar. One single camera. Runs in real time & is open source: A streaming 3D model that reconstructs scenes live, at ~20 FPS, over long sequences. End-to-end. Optimization tricks, cleanup steps? Nope. And it beats both streaming and even some offline methods. Perception is becoming software-first. Closer to machines that see and understand the world as it unfolds. Thanks for sharing, @YinghaoXu1 📍Models: Project page: Code: Paper: —— Weekly robotics and AI insights. Subscribe free:

I had the opportunity to work together with @ditzikow, partner at @spc, during tech week NYC earlier this year, as they were hosting our robotics event 🫶 He was so kind to give me an interview, about their mission, building a community of builders and supporting researchers and founders in tech. Worth a read!!

Incredible how much the ‘post video’ function sucks! Especially after @nikitabier told us over and over again how important it is to use it, instead is downloading the videos and post them again… Whether you use a link, it’s not working. When do you guys fix this??

Crazy how far robot foundation models have come in terms of true fine motor skills: Without task-specific training, no sim-to-real transfer, and... fully autonomous ❗ The dual-arm robot places an O-ring onto a hydraulic rod and removes it again in a fully autonomous and repeatable manner, with frequent contact, precision, and without task-specific training. The model uses short physical prompts (a demonstration lasting just a few seconds) for one-shot learning and adapts to new manipulations in just a few gradient steps. No sim-to-real transfer is necessary; instead, pretraining is performed on real interaction data. It is precisely these types of tasks (O-rings, seals, small components) that are classic bottlenecks in assembly. If this can be reliably scaled up, the cost-effectiveness of robots in manufacturing will change significantly. Source: @GeneralistAI / Congrats to @peteflorence, @andyzengineer and the team on the GEN-1.5 demo!

What does it take to build one robot actuator? We mapped it from the mine to the supplier. - 52 named suppliers, sourced from filings and company disclosures - 66 parts, processes and materials across 9 stages - Rare-earth magnets: 8 named makers and the refining chain behind them - Steel connects to 48 items in the map - One ring gear traced to the machine that cut it Click any part to see what it depends on. Hit “Simulate a shortage” to watch it spread. This is v0.1. We add suppliers as each one is sourced. Explore the interactive page in the comment, and please share any feedback ! Follow @CoreMatter_ for physical AI deep dives.

A reminder of what industrial automation already looks like at scale. Multiple robots, tightly choreographed around a car body, each doing one very specific job with incredible precision. Now imagine adding perception, adaptability and learning to systems like this. That’s what I'm excited about, and that's also the reason I am getting more and more back to my manufacturing roots and talking to manufacturers these days. 📍 Toyota Commemorative Museum of Industry and Technology, Nagoya Video: @jorge_fandos_

BERLIN – OCT 22 – ROBOTS, FREE BEERS, AND NERDS 🦾 @prototype_cap is hosting the inofficial-official Afterparty of NVIDIA GTC BERLIN 2026 together with @Microsoft and @NVIDIA 🔥 Robots, free beers, bratwurst, live demos, pitches, and tons of robotics founders, students, and investors. Also the team of @esra_robotics is coming! If you are into physical AI, robotics, or happen to be in town for GTC – come by. If you have a machine you want to show DM me. Please sign up asap, as spots are super limited. RSVP Link: 🔥🤖

Coming back from SF, I just want to say thank you to everyone who was kind enough to show me what you’re working on. It means a lot that you gave me access to your worlds. Places that look totally normal within our bubble, but are truly the future for most people out there, including me. I can’t wait to share more of the incredible work these people are doing, their enthusiasm, their passion, and, of course, their tech!

A bidirectional teleoperation system for dexterous robot hands. It lets you control a robot hand, and the robot hand can also move your hand. When the robot makes a mistake, it first moves your hand to match its position. That way you can take over smoothly without dropping the object. These corrections help the AI get better. It works with different robot hands, gets better results than regular gloves, and comes with a paper and a dataset. Thanks for sharing, Zhanpeng He (@zhanpeng_he) 📌 Paper: Website: —— Into robotics and AI? Subscribe free:

Five iterations and six hours. Enough to fine-tune a humanoid motion generator with RL. 📌 For anyone working on humanoid locomotion or generative robot policies, this paper is worth saving. The PPO baseline used more than 160x the data and 30x the wall-clock time, and still ended up about 13 points lower in success rate. The work comes from Zachary Olkin, William Compton and Aaron Ames at Caltech's AMBER Lab. Their Unitree G1 plans 1.24 seconds of whole-body motion directly from depth images and replans every 0.24 seconds, while a separate tracker runs at 50 Hz. The generator produces 2,728 values for every plan, which makes ordinary action-space exploration extremely inefficient. Instead of adding Gaussian noise across the whole output, they perturb the conditioning, resample the initial flow-matching noise and start the robot from states the original policy would rarely visit. The nominal conditioning is kept as the label. A critic is trained on the generator's frozen prefix encoder and assigns advantages to the collected trajectories. Those advantages weight the flow-matching loss, giving better trajectories more influence on the next version of the generator. The reward is built around terrain consistency. Penetration and contact are checked first using signed distance fields, while velocity tracking and task success only count when the planned motion is consistent with the terrain. After five iterations, success on unseen geometries and skill combinations improved by as much as 25 percentage points. On one staircase, the share of runs that selected every required skill correctly went from 0% to about 80%. The ablations are useful here. Plain behavior cloning performs poorly, removing the conditioning perturbations costs around 4 to 6 points of success, and PPO on the full 2,728-dimensional output needs far more data while still performing worse. The camera setup also shows up clearly in the numbers. With both depth cameras, box success in simulation is 100%. With only the lower camera, it falls to 57.3%, because the upper camera gives the robot enough warning to slow down before a box or staircase and speed up again afterward. The full system runs without odometry or height maps. Depth images go into the motion generator, the tracker follows the generated motion, and the fine-tuning loop improves the generator from collected rollouts. Website: Paper: Video: —— Weekly robotics and AI insights. Subscribe free:

Robots are 3D printing full-size ship hulls. ㅤ CEAD’s Faber Navalis system uses robotic arms to print complete marine hulls up to 12 m long and 4 m wide in a single automated run—no molds, no segmentation, and no heavy manual assembly. ㅤ A 12-meter RIB-style prototype for the Royal Dutch Navy was printed in 8 days, tested during NATO’s BOMA exercise, and sailed for four days with the Navy in Den Helder. Traditional delivery timelines for similar vessels often run into years. ㅤ The hulls use glass-fiber-reinforced HDPro, roughly 10% lighter than comparable fiberglass while offering higher impact resistance than standard HDPE, plus UV stability and recyclability. Primary targets are USVs, RIBs, workboats, and patrol craft. —— Weekly robotics and AI insights? Subscribe free:

Hands down, this hands-on session is one of the coolest I’ve been so far! Today we got to train a robot on Mars. Well, in simulation. The mission: retrieve a fuel cell 200 meters away and bring it back before life support runs out. There was one problem. The quadruped already knew how to walk, but it had been trained for Earth. Earth gravity: 9.81 m/s². Mars: 3.72 m/s². Put the same policy on the Martian surface and the robot basically trips over itself. So we had to teach it how to walk again. We used reinforcement learning, specifically PPO, trained the policy on CoreWeave infrastructure and tested it in simulation. Then you look at what happened, change the training setup and run it again. Sometimes the robot walks. Sometimes it does something completely stupid. That was probably my favorite part. What I liked about the workshop was actually seeing that loop end to end instead of another slide about Physical AI. Led by Anushrav V., @CoreWeave’s Robotics Field Engineering Lead, at Fully Connected.

A robot arm can now be trained over a weekend by three students in San Francisco. A bulldozer cannot. Spent part of the day on the floor at Fully Connected, @CoreWeave's conference in San Francisco. Good energy, plenty of cool demos. My favourite panel: From World Models to Working Machines: Physical AI in the Real World Caterpillar Inc., @nvidia, and CoreWeave, talking about what it actually takes to get physical AI out of the lab and onto a job site. Brandon Hootman, who runs construction autonomy at Caterpillar, said the company's development cycle used to run in years, timed to when a new machine model launched. It now runs in months. His reasoning: Caterpillar stopped waiting for the next product generation to test what it was learning, and started iterating with customers directly on real sites instead. NVIDIA's Amit Goel explained why this only became possible in the last year or two. Models turned multimodal, so they read video and lidar, not just text. Simulation got trustworthy, combining physics engines with models trained on real footage. And the compute to run both finally showed up. His number for the data gap... one hour of real machine data can now generate somewhere between 100 and 1,000 hours of usable simulation. The hardest part to simulate, according to Goel, is contact. What happens physically the moment a bucket hits soil or a truck bed meets rock is where simulations used to fall apart. His team now feeds real field data back into the simulator constantly to correct that. Hootman added one interesting thing. Caterpillar didn't rip out its existing safety and control systems to make room for AI. The model operates inside them, with hard limits on what it's allowed to decide on its own. Richard Ahlfeld, Ph.D. from CoreWeave made the infrastructure case: the data volume here runs far beyond what language models deal with, and evaluation has to sit right next to training, or you're flying blind on whether the model actually works. Working demo and something running unattended in the field... what's your best story here?