How to Build Clean Demonstration Datasets for Imitation Learning in Robotics

Imitation learning projects often succeed or fail based on the quality of their demonstration datasets, where noisy or inconsistent recordings lead to equally flawed trained policies. A demonstration is defined as a single, complete task episode capturing synchronized observations and actions, structured with metadata such as task labels, success flags, and operator IDs. Key design decisions before recording include choosing observation and action spaces, a consistent sampling rate of 10–30 Hz, and a file format such as HDF5 or per-episode directories. Operators should explicitly label each episode as a success or failure rather than relying on automated detection, which is considered less reliable in early stages. Experts recommend prioritizing a smaller set of clean, diverse demonstrations over large repetitive datasets, varying initial conditions and involving multiple operators to improve policy generalization.