LeRobot: AI models and datasets for robotics

LeRobot is a PyTorch-based library for robot learning that provides pre-trained models and datasets for imitation learning and sim-to-real transfer applications. The framework implements policy learning architectures such as diffusion models that accept multi-modal observations including image and state data, applying configurable normalization strategies like mean-std for vision inputs and min-max scaling for state vectors. Training occurs through standard PyTorch optimization loops where batched data from HuggingFace-hosted robot demonstration datasets flows through the policy network to compute loss values for gradient descent updates. The library supports distributed data loading with configurable worker processes and enables GPU acceleration by moving tensors and model parameters to CUDA devices. Trained policies can be serialized and stored using the pretrained model format for subsequent deployment or fine-tuning.