How to Build an Imitation Learning Pipeline for Robotic Manipulation

A new technical tutorial outlines an end-to-end imitation learning pipeline for training robots to perform manipulation tasks using behavior cloning. The pipeline covers key stages including data preprocessing, dataset construction, model architecture selection, training loops, and policy evaluation. Developers are advised to normalize action data and image inputs before training, as inconsistent scales can destabilize the learning process. The tutorial recommends structuring datasets as short observation windows rather than single frames, and predicting sequences of future actions rather than one step at a time for smoother robot behavior. Architecture choices such as using a visual encoder paired with proprioceptive state inputs and a transformer-based sequence model are highlighted as effective baselines for image-based manipulation tasks.