Aurevex — Physics‑Aware Data Labeling for Robotics Robotics teams aren’t blocked by models anymore — they’re blocked by data. We built Aurevex after watching teams hit the same wall over and over again: Human teleoperation and manual labeling that costs thousands per clip Days or weeks of turnaround that slow experimentation Datasets that label pixels, but miss real‑world physics like force, torque, and contact Aurevex replaces that bottleneck with an automated, physics‑aware data labeling engine designed for Vision‑Language‑Action (VLA) robotics. What’s different? ⚡ 10–100× faster training‑ready datasets 💸 ~90% cheaper than human teleop or vendor labeling 🧠 Labels grounded in physics, not guesswork — force, torque, contact, micro‑slip 🔗 Native multi‑sensor alignment (RGB, depth, LiDAR, proprioception, tactile) No humans in the loop. No linear scaling. Just compute. Who it’s for Robotics teams working on manipulation, autonomy, or long‑horizon tasks who are tired of data being the slowest, most expensive part of the stack. We’d love feedback from this community: Where do data costs or iteration speed hurt you most today when training robots? Happy to answer technical questions in the comments 👇

Aurevix

Automated physics-grounded pipelines for real-world robotics

Automated physics-grounded pipelines for real-world robotics