One of our first autonomous successes on the gland assembly. How it works: we teleoperate the task first. That becomes training data for our model, which then attempts it autonomously. When it makes mistakes, we intervene and show it how to recover. Those corrections make the policy more robust and push the success rate up. The model runs end to end, down to switching the screwdriver on and off. Every task we run on the real robot grows the dataset. The bet: the more diverse it gets, the less data a new task needs.