For scaling up data generation, they use a language model to guide high-level planning and sampling-based robot planners to generate rich and diverse manipulation trajectories (b). To robustify this data-collection process, the language model also infers a code-snippet for the success condition of each task, simultaneously enabling the data-collection process to detect failure and retry and automatically label of trajectories with success/failure (c).