Fine-tuning robots on operational data
A robot that is deployed and then left alone gets worse relative to the work around it. Ours get retrained.
A deployed robot sees new parts, new packaging and new layouts every week. A model trained once and shipped slowly falls behind the work it is supposed to do.
Our deployments close that loop. Every episode on the floor, successful or not, becomes data. Failures are reviewed and labeled, and the model is retrained on the site's own recent history.
New versions never go straight to the fleet. They run in shadow mode first, then on a small share of robots, and only reach the full fleet once they beat the current version on measured task success.
The effect compounds. Each site's data improves the model for that site, and shared learnings improve every deployment.