[R] Kinematic Fingerprints: Predicting sim-to-real transfer success from movement signatures

We’re working on predicting whether a policy trained in simulation will transfer to real hardware — without testing on the real robot.

Approach:

• Extract kinematic features from sim rollouts (joint trajectories, accelerations, torque profiles, jerk)
• Encode to fixed-dim fingerprint via temporal CNN
• Contrastive learning: successful transfers → similar fingerprints
• Classifier predicts transfer probability for new policies

Results: 85-90% accuracy on held-out policies. Generalizes across robot platforms (7x deployment speedup).

Key insight: the fingerprint captures behavior robustness, not task completion. Smooth, compliant policies transfer. Brittle, exploit-the-physics policies don’t.

Writeup with more details: https://medium.com/@freefabian/introducing-the-concept-of-kinematic-fingerprints-8e9bb332cc85