Generalist AI has released GEN-1.5, a robot foundation model that learns a new physical task from a single demonstration. Drop 3–12 seconds of sensorimotor data into its 30-second context window, and the robot performs the task. No gradient updates, no fine-tuning, no task-specific programming. Across 10 diverse manipulation tasks, this one-shot in-context prompting averaged 59% success (±10% std. dev.) straight from the pretrained model. Ten gradient steps on five minutes of data per task raised that to 83% (±9%). Generalist calls the mechanism physical prompting, and says it was never trained for: no architectural changes, no meta-learning loop, no auxiliary objectives. It emerged from over eight months of continuous pretraining on physical interaction data. The tasks are simple and short-horizon, and the company says so plainly. But this is the first model its team knows of where one-shot learning of physical skills has emerged at scale.
Is it deployable?
Not yet — this is a research release. There are no public weights, no API, no pricing page and no self-serve product. Generalist AI runs GEN-1.5 on its own fleet and data engine. Anyone who wants it today goes through a direct partnership.
