Tutorial: How to Build an End-to-End Imitation Learning Pipeline for Robots

A new technical tutorial published on DEV Community guides developers through constructing a complete imitation learning pipeline for robotic manipulation tasks. The pipeline covers dataset loading from recorded HDF5 demonstration files, preprocessing, model architecture design, a training loop, and rollout evaluation. A key component is action chunking, where the model predicts several future actions at once rather than a single step, a technique shown to reduce compounding errors in modern imitation learning. The proposed model architecture pairs a CNN-based vision encoder with an MLP head that jointly processes camera images and robot joint states to predict action sequences. The tutorial also emphasizes action normalization as a critical but often overlooked step, advising developers to save normalization statistics alongside model checkpoints for consistent inference.