Researchers At Stanford Introduce Robofume: Revolutionizing Robotic Learning With Minimal Human Input

This article discusses the challenges of using the pre-train and fine-tune paradigm in robotics, which involves pre-training a general-purpose model from an existing dataset and then adapting the model with a small addition of task-specific data. It proposes a framework that allows robot fine-tuning with minimal human and time-consuming effort, and highlights the advancements made in developing effective and self-governing reinforcement learning algorithms.