PrismaX

Introducing the PrismaX Regional Ambassador Program. A select cohort of regional leaders building local PrismaX communities in their language, region, and time zone. Applications open today 👉 forms.gle/3Hfo8yEEKsGLeo…

@FortuneMagazine Physical AI scales when the deployment layer is shared. One robot in a lab is an experiment. Eight operators in one district is infrastructure. The standards are what make that work.

Southeast Asian tech company Grab will launch a pilot of its first delivery robot in Singapore’s Punggol district in late 2026, as part of a deeper push into physical AI and robotics. bit.ly/4dC9oF1

@ny14co Daily reps build operator muscle memory. That's what compounds into data quality 🦾

I’ve been visiting @PrismaXai every single day lately. Not just for points… but because every time I open the platform, it genuinely feels like I’m interacting with a piece of the future. From doing daily tasks, exploring different sections of the site, testing features, and watching how robotics + AI + blockchain are being connected together… you slowly realize this project is operating on a completely different level. Most projects talk about innovation. Prisma is literally letting people experience it. The robotic control section especially blew my mind. It doesn’t feel like a simple Web3 dashboard anymore , it feels like the first step toward real-world decentralized infrastructure. And honestly? Projects like this are rare. You can feel the vision behind every detail of the platform.

@nixkhhil Right! Quality beats quantity when "quality" means data that matches the model you're actually training.

A few days ago I read an interesting article from @PrismaXai about robotics training data. That article got me curious so I went deeper into the topic to understand what they actually meant. One thing became very clear. Not all robotics data is solving the same problem. ─────────────── PrismaX explains that robotics AI is split into two very different worlds The first is 𝗸𝗶𝗻𝗲𝗺𝗮𝘁𝗶𝗰𝘀 𝗺𝗼𝗱𝗲𝗹𝘀. These are low level systems trained with reinforcement learning and simulation. They focus on precise robot control and usually work on one specific robot performing one specific behavior. Things like: • jumping • balancing • galloping • fixed movement patterns They look impressive in demos, but they do not generalize well to new robots or new tasks. The second category is 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝘀 This is where most of the new Physical AI race is happening. Foundation models work more like LLMs. They learn from massive amounts of: • video • actions • sensor signals • task demonstrations The robot learns to predict movements, forces, and actions from observations and prompts. The goal is not one trick. The goal is general intelligence across environments, tasks, and even different robot bodies. As PrismaX put it, most people do not want a robot doing backflips. They want one that can do the dishes. ─────────────── [Why the data collection method matters] For foundation models, the quality of the [video + action] pair is everything. PrismaX highlights three major ways robotics data is collected today. • Teleoperation Humans remotely control real robots. Highest quality and no embodiment gap because the robot learns directly from robot actions. Most current foundation models rely heavily on this approach. • Human videos People simply perform tasks naturally on camera. Cheap and scalable, but human hands do not perfectly map to robotic hardware. This creates a cross embodiment gap. • Gripper or glove systems A middle ground where humans use tracked grippers or wearable systems. Faster and cheaper than full teleoperation while reducing embodiment mismatch. ─────────────── [Recent research is starting to validate this] Projects like UMI, ACT 1, and GEN 1 are already showing strong results using gripper style and wearable data systems. Some systems collect data several times faster than traditional teleoperation while improving task success rates dramatically. ─────────────── [Why PrismaX still prefers teleoperation] According to PrismaX, teleoperation still produces the cleanest and most reliable data for foundation models. Better trajectories and better supervision help models converge faster during training. Instead of collecting random large scale datasets, they focus on carefully curated high quality interaction data. ─────────────── [My biggest takeaway] The robotics industry talks constantly about “more data.” But PrismaX made an important point. The real question is not how much data you have. It is what kind of model you are actually training and whether your data truly matches that goal. In robotics, quality beats quantity far more often than people realize. All the useful links for PrismaX- X Handle - @PrismaXai Discord - discord.gg/prismaxai Linktree- t.co/ipHnnMfn5Q @vivianrobotics

Or listen to the episode on your favorite platform ⤵️ Spotify: open.spotify.com/episode/1FZxjN… Apple Podcasts: podcasts.apple.com/us/podcast/pai…

Episode 1: Painting Robots, Force, and the Industrial Graveyard with Dane Kouttron. Why robotics is still bottlenecked on force, not models. Why VLMs run at sub-240p. The graveyard of cheap industrial robots waiting to be reused. Watch here ⤵️ YouTube: youtu.be/wyVHwKQbdPo

Doesn't Grasp is live 🎙️ A new monthly podcast on what's actually happening in robotics and physical AI. Hosted by @castorhat, sponsored by PrismaX. Technical, unscripted. The conversations you can't get from viral tweets.

Learn more about the program ⤵️

2️⃣ Submit your quote post link and complete the form: forms.gle/3Hfo8yEEKsGLeo…

1, 2, 3 days left to apply for the PrismaX Regional Ambassador Program 🚨 Applications close May 24 at 11:59 PM EDT. Rolling review. Apply now, get reviewed now. Don't wait for the deadline. Here's how to apply 👇

@vivianrobotics @BiggbossForeig @castorhat Community engaging with the technical content is exactly the loop we want 👏 The full article goes deeper on what separates high-quality teleop data from noise.

Thank you @BiggbossForeig for the recap 🙌 This week’s Trivia Tango was based on @castorhat’s latest article: “Not All Robotics Data Is Created Equal.” 🧠 One of my biggest takeaways: ChatGPT became intelligent from internet-scale text. Future robotics foundation models will become intelligent from internet-scale real-world actions 🤖 Not robots that only backflip. But robots that understand, adapt, and interact with the physical world. @PrismaXai is building the data layer powering that future. Congratulations to all the winners 🏆

@akratomi 🦾

Robots Don’t Need More Sensors: They Need Better Understanding. Robotics teams often respond to failure by adding more sensors, more cameras, more LiDAR, more depth or more force feedback. But this rarely solves the real problem. Robots don’t fail because they lack sensors, they fail because they lack understanding, the ability to interpret the world consistently. A robot can have 10 cameras and still misjudge a grasp. It can have perfect depth maps and still fail to place an object correctly. The missing piece is structured, high quality perception to action data. @PrismaXai focuses on improving the interpretation layer for: ➛ Better data pipelines ➛ Better labeling ➛ Better action traces ➛ Better context metadata ➛ Better feedback loops Understanding beats raw sensing because intelligence is built on meaning, not pixels. Use cases where better understanding outperforms more sensors are: ⭆ Warehouse picking with cluttered bins ⭆ Mobile robots navigating dynamic spaces ⭆ Industrial arms handling deformable objects ⭆ Service robots interacting with humans The future of robotics isn’t about adding more hardware, it is about extracting more intelligence from the hardware we already have and I can tell you @PrismaXai is building the layer that turns sensing into understanding. To have more understanding, go to - prismax.ai @vivianrobotics @castorhat

@CyberRobooo Humanoids deployed where the stakes are high!

China Huadian (CHD), an energy and power company, is using a humanoid robot developed based on the Unitree G1 to perform circuit breaker closing tests. This is a high-risk operation with a very high potential for high-voltage electrical accidents, traditionally requiring two workers to collaborate. Yet danger is always present. The video is short, and I’ve edited in clips of real accidents for comparison. The humanoid robot is now in the full loop of inspection and breaker closing. Just as shown on the right side of the video, workers can observe safely from a distance, fully removed from the danger.

@JoyJuliet_AI The gap between physical AI in a lab and physical AI in 1,400 stores is operational data. Real environments produce the signal models need.

Everyone is building AI for screens. Almost no one is building for physical AI. This company just raised $170M to prove why hard tech is the next trillion-dollar frontier. Here's everything to know 🧵

@Cointelegraph Full-body teleoperation with balance maintenance. The fidelity of the operator-robot link determines the quality of data these sessions produce. This raises the bar.

🤖 NEW: Japanese robotics startup Tokyo Robotics unveiled its Torobo humanoid robot, capable of smooth human-like walking and real-time full-body teleoperation while maintaining balance.

@IlirAliu_ Failures and recoveries in the training data. That's the part most pipelines skip. Real-world operator sessions capture exactly this (the corrections, not just the completions).

15–30 minutes of real-world robot data. That's now enough to go from sim-to-real failure to working robot. Let’s see… You train a robot in simulation. You deploy it in the real world. It fails. The physics don't match. So you try to fine-tune it with real data, but… you never have enough real data, and the fine-tuning breaks everything the simulation taught it. SimDist fixes this with one key decision: don't transfer the policy. Transfer the world model. Keep the reward and value knowledge from simulation frozen. Only update the part that's actually wrong, how the robot predicts physics. Now the robot doesn't have to relearn the entire task in the real world. It already knows what success looks like. It just needs to correct its understanding of how the real world moves. The part that makes this work: they also trained on failures and recoveries; not just perfect demonstrations. Without that, the planner finds the gaps and exploits them. With it, the robot can tell a good future from a bad one. That's all it needs. Results on peg insertion, table leg assembly, locomotion on slippery and uneven surfaces. Tasks that require precision, force, and quick reaction. Thanks for sharing, Tyler Westenbroek ([@ty_westenbroek]. Interactive visualization + paper: sim-dist.github.io ——- Weekly robotics and AI insights. Subscribe free: 22astronauts.com

If you’ll be in town, we’d love to see you there! RSVP ⤵️ partiful.com/e/GxfjRxDRUK8w… #NYTechWeek

NY Tech Week is one of the best moments each year to bring together people building at the frontier, and we’re excited to contribute to the ecosystem here in New York.

Robots. Crypto. Cocktails. NYC. We’re excited to host Robots & Rollups during @a16z #NYTechWeek 🍸🤖 The only #NYTechWeek event bringing physical AI and crypto under one roof 👉 partiful.com/e/GxfjRxDRUK8w…