A Coding Guide to End-to-End Robotics Learning with LeRobot: Training, Evaluating, and Visualizing Behavior Cloning Policies on PushT

The article highlights the use of Hugging Faces LeRobot library to facilitate end-to-end robotics learning through behavior cloning on the PushT dataset. By leveraging LeRobots unified API within Google Colab, researchers can efficiently load datasets, design compact visuomotor policiescombining convolutional neural networks with small MLP headsand train models that map visual and state observations directly to robot actions. This approach emphasizes reproducibility and rapid experimentation, enabling users to develop dataset-driven robot control policies with minimal setup. The key innovation lies in LeRobots streamlined pipeline, which simplifies the process of training, evaluating