标签：#robotics

I just wrote about the billion-dollar rounds flooding into humanoid robotics. Here is the story from the other end of the scale, and I find it more encouraging. Hugging Face's open-source robot, a 25-centimeter bipedal machine with fifteen actuators and a sensor kit that includes a camera, speaker, LiDAR, NFC, Bluetooth, and WiFi, just passed a million dollars in sales. Fully open hardware, openly documented, quietly making real money. One of these robotics stories is funded like an industrial giant. The other is a small, open, shippable thing that people are actually buying. They are both true, and the small one is the one most builders can learn from. Open hardware turned out to be a business The reflexive assumption about open-source hardware is that you cannot make money on it, because anyone can copy the design. Hugging Face's robot is a live counterexample. The plans are open, the software stack is open through their LeRobot ecosystem, and it crossed a million in sales anyway. That is worth sitting with, because it means openness and revenue are not the opposites people assume. The reason it works is the same reason open-source software companies work. Most buyers do not want to source fifteen actuators, fabricate a chassis, and debug a sensor stack to save money on a robot that already exists and is affordable. They want the finished thing, they want it to work out of the box, and they are happy to pay the people who designed it. Openness is not the giveaway that kills the business. It is the trust and the ecosystem that make the business, because you can see exactly what you are buying, modify it, and build on a platform other people are also building on. Why this is the better story for builders The mega-funded humanoid companies are placing a bet only a handful of players can place: billions of dollars, years of runway, factories. That is a real path, and it is not your path or mine. The Hugging Face robot is the other path, and it is copyable. Small, open

Technical progress has encouraged a new batch of companies to jump in on the promise of profits from humanoid robots. And they're all Chinese automakers.

Hugging Face is taking orders for the Microduck, a $399 tiny open-source duck robot that developers can train at home out of the box.

If you've ever wanted a robotic duck as a companion, now's your chance.

Perceptron offers an AI model that it says can help machines navigate the world while also providing in-depth visual intelligence.

Robot bodies are waiting for their AI brains to catch up.

The $200 million extension comes just months after the physical AI startup reached a $2 billion valuation.

Record-breaking robot races are less substantial than household chore challenges.

And they looked absolutely ridiculous doing it.

During a recent visit to Generalist AI, I watched a robotic arm improvise and use a banana as a tool.

The Mission Robotic Vehicle is making the first attempt to attach a new thruster to an aging satellite.

The divestiture comes as the two once-tight companies have started to diverge on the business side.

"Smile because it happened" — Dr. Seuss The Setup Earlier this year, I took on a short-term trial role with an early-stage robotics startup. The premise was straightforward: help with data collection, annotation, and evaluation workflows—essentially the backbone of any modern robotics or embodied-AI system. The trial didn't work out long-term. I was let go after about two months — a decision that, honestly, came down in part to my bandwidth as a student. Balancing a full course load with a startup trial was harder than I anticipated. But that's not the story I want to tell. What I do want to share are the technical lessons I took away — lessons about building robust data pipelines, about the gap between theory and practice, and about what I'd do differently next time. These aren't company secrets. They're about the general engineering challenges that anyone working with robotics data pipelines will encounter — challenges I'd read about in papers but hadn't truly internalized until I was standing in front of them. 1. The Data Pipeline Shape Is Universal—But the Details Aren't If you've spent any time in ML or robotics, you've seen this described: Data Collection → Annotation → Evaluation It's a standard three-stage pipeline. Industry vendors describe it explicitly in their robotics content. Academic projects model this structure. It's the field's shared vocabulary. Companies such as Scale AI and Toloka use similar industry workflows involving data collection, annotation, and evaluation. What isn't shared are the specifics: the sensor setup, the calibration procedures, the annotation rubric, and the evaluation metrics. Those are where a company's IP lives. The pipeline shape? That's just the map. And the map is public. What I'd do differently: Simulate before you collect. Data collection is expensive — in time, hardware wear, and cognitive load on operators. Before running a full session, run a feasibility study with a small batch. Verify your sync and capture scripts

July 2026 is over. The month that opened with AUTONOMOUS 2026 and WAIC 2026 running simultaneously on opposite sides of the Pacific closed with the sector tallying what it built. The number that defines the period is $55.8 billion in robotics funding across H1 - nearly double the prior full-year record. But the more durable signal from this week is operational rather than financial: Neura Robotics has a confirmed deployment date at a Schaeffler facility in December, NVIDIA's simulation-to-real pipeline is now functional at production scale, and five simultaneous shifts are reshaping factory floors right now, not in 2027. The questions that drove the first half of 2026 - does Physical AI work, is the funding real, will the robots actually arrive - are no longer interesting. H2 starts with harder ones. Stats: Value Description $55.8B Robotics funding raised in H1 2026, nearly double the prior annual record $8.6B Humanoid startup funding in H1 2026 alone, 1.8x all of 2025 December 2026 Confirmed first deployment of Neura Robotics humanoids at Schaeffler's German facilities 5 Simultaneous operational shifts reshaping factory floors identified in the mid-2026 analysis Neura Robotics Has a Deployment Date: December 2026 in a Schaeffler Factory Most Physical AI deployment announcements are directional. "We are partnering with X to explore robotics in our facilities" is a press release. A confirmed month and a specific facility is a contract. Neura Robotics confirmed that Schaeffler - one of the key investors in its $1.4 billion Series C alongside Amazon, Nvidia, Qualcomm, and the European Investment Bank - plans to deploy Neura's humanoids in its German facilities in December 2026 . Schaeffler manufactures precision bearings and components for electric vehicles, operating in environments where dimensional tolerances are measured in micrometers. Deploying a humanoid robot in that context is a fundamentally different challenge than warehouse pick-and-place or automotive sequ

Researchers fear AI is moving too fast, while Mark Zuckerberg is worried about who owns it. Plus: Inside Black Forest Labs’ push into robotics.

Gemini Robotics 2 includes three models, but only one is publicly available right now.

The massive seed round was led by Index Ventures and Ribbit Capital, with participation from Sarah Guo's Conviction Partners.

Forget YouTube videos—frontier physical AI models need multiple camera angles, dense annotation, and soon, brain wave readings.

US robotics researchers flew to Venezuela with snakebots after getting a call.

Gritt is coming out of stealth with $34 million and plan to automate the hardest tasks on construction sites.