Your robot has an accent — why some sim-trained policies transfer and others faceplant

** These are ALL my ideas. LLM’s only used fo slight ‘polishing’. **

Been working on predicting sim-to-real transfer success BEFORE deploying to real hardware.

The insight: successful transfers have a distinct “kinematic fingerprint” . Smooth, coordinated movements with margin for error. Failed transfers look jerky and brittle.

We train a classifier on these signatures. Early results show 85-90% accuracy predicting which policies will work on real hardware, and 7x speedup when deploying to new platforms.

The uncomfortable implication: sim-to-real isn’t primarily about simulator accuracy. It’s about behavior robustness. Better behaviors > better simulators.

Full writeup: https://medium.com/@freefabian/introducing-the-concept-of-kinematic-fingerprints-8e9bb332cc85

Curious what others think. Anyone else noticed the “movement quality” difference between policies that transfer vs. ones that don’t?