Experiments across 10 diverse tasks show 59% average success with one-shot physical prompting, straight from pretraining. With few-shot learning, performance rises to 83% via 10 gradient steps on 5 minutes of data per task.
Although the tasks are simple and success rates are modest, it’s the first model we know of that exhibits the general ability to learn a wide range of dexterous closed-loop physical tasks from just one or few demonstrations. This accelerates reaching a base level of competence for new skills that can be subsequently refined towards mastery.