TL;DR
This article explores the process of teaching a robotic arm to perform tasks autonomously through a method called Imitation Learning. It outlines a comprehensive workflow that includes calibration, teleoperation, dataset collection, training with Action Chunking with Transformers (ACT), and finally, deploying the robot in real-world scenarios. The focus is on a specific project using the SO-ARM101 robotic arm, which can complete pick-and-place tasks without human intervention after training.