I Built a Physics Certification Layer for Motion Data — Here's What I Found

TL;DR: I trained a classifier on robot motion data and kept getting weird failures. The data looked fine. It wasn't fine. So I wrote a tool that checks whether sensor data actually obeys the laws of physics before you train on it. Here's what I learned. The Problem Nobody Talks About When you train a model on images or text, bad data is annoying but recoverable — you clean it, you re-label it, you filter it. The model is usually forgiving. When you train a physical AI system — a prosthetic hand, a robot arm, a rehabilitation exoskeleton — bad training data doesn't just hurt accuracy. It teaches the system physically impossible movement patterns. A prosthetic hand trained on corrupted EMG data fails the person wearing it. A humanoid robot trained on synthetic motion data that violates rigid-body kinematics learns to move like a cartoon. The problem is that most motion datasets have no quality floor. They contain: Synthetic data generated without real sensors (no actual physics coupling)