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.

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

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

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 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

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

Our data capture pipeline sneak peek: human operator in Meta Quest, Franka arm in sim, photorealistic rendering across multiple environments, egocentric camera output. Exylos #PhysicalAI #Robotics #Innovation

Robotics data dilemma: egocentric video gives you real intent but raw footage only. Pure synthetic scales cheap but has zero human reasoning. The middle ground — capturing human intent inside sim — gives you both. #PhysicalAI #Robotics #EmbodiedAI