Ding Hao (Xuanwo)

Robot datasets grow not only by adding new episodes, but also by accumulating derived columns such as embeddings, labels and rewards as new models become available. Traditional approaches often require rewriting entire datasets and depend on fragile job orchestration.

This talk applies build-system ideas to robot data: precisely tracking work that has not yet been computed, incrementally processing missing ranges, resuming interrupted jobs, atomically publishing results, and pinning data versions for reproducible training. With LanceDB, teams can continually update a robot’s understanding of historical data without repeatedly rewriting the raw data.