Exylos

Every robotics team has the same problem: not enough training data, no fast way to get it. That's what we're here to fix. Exylos — a data engine for Physical AI. Human demos in VR → thousands of transfer-ready training episodes. exylos.ai #PhysicalAI #Robotics

Robotics sim tools are converging on great physics. But most deployment failures aren't physics failures — they're intent failures. The robot doesn't understand why a human approached the task that way. Physics you can simulate. Intent you have to capture. #EmbodiedAI #Robotics

In physical AI, data quality beats data volume. The winning stack is synthetic + teleop + real-world failure data, tightly filtered and looped back fast. Biggest advantage in 2026 isn’t model size - it’s who turns messy reality into clean learning fastest. #Robotics #EmbodiedAI

Newton 1.0 is now generally available. 🙌 Take robot learning to the next level with: 🤖 Stable Articulated & Complex Mechanism Simulation – accurate, reliable machine modeling. 🖐️ High-Fidelity Hydroelastic Contact Modeling – realistic soft contact and touch-based interactions. 🧵 Deformable Body Simulation – simulate cables, cloth, rubber, and other elastic materials with VBD. ⚡ Accelerated Robot Learning at Scale – seamless integration with open simulation and learning frameworks, NVIDIA Isaac Sim and Isaac Lab for scalable workflows. Learn how to integrate this open-source physics engine into your workflow: nvda.ws/3NGTzUo #NVIDIAGTC

Sim-to-real gap isn’t a research excuse anymore - it’s an operating KPI. Track cost per failed transfer, time-to-fix, and redeploy speed. If simulation doesn’t reduce real-world failure cost, it’s just expensive theater. #Robotics #PhysicalAI #MachineLearning

In robotics, model quality matters — but data loops win. Teams with faster capture → clean labeling → feedback iteration will beat teams with bigger models and slower learning cycles. In 2026, speed of learning is the real moat. #Robotics #PhysicalAI #machinelearning

If cloud AI had hyperscale data centers, what becomes the equivalent for Physical AI? How do we fill these pipes at scale? #PhysicalAI #Robotics #EmbodiedAI

The pipes are being laid, but they are still mostly empty. Turning raw interaction into reliable learning loops that hold up across sim and deployment is where the real race is now.

A market-level observation from this news cycle: Capital and engineering effort are converging on Physical AI infrastructure. What looked like separate announcements is actually a single stack forming in the wild. 🧵 #PhysicalAI #Robotics #EmbodiedAI

Building an XR-native pipeline for robotic skills: VR intent capture → QA gates → sim amplification & validation → robot-ready episodes. The goal is NOT more raw data. It is to lower deployment uncertainty in HMLV workflows.