Exploring Pre-training and Fine-tuning in Robotic Models

This chapter explores the pre-training and fine-tuning processes of the TESA Act, emphasizing the effectiveness of a well-pre-trained model on specific tasks like robotic tea pouring. It also addresses the model's limitations in generalizing to vastly different tasks, such as playing tennis, showcasing the hurdles in achieving successful zero-shot learning.