Balancing Data in Robotic Training

This chapter explores the integration of real-world, task-aware, and task-agnostic data in training robotic systems. It emphasizes the significance of sampling ratios, data weighting, and the role of simulation to enhance robot performance across various tasks. Through detailed analysis and experimental insights, the chapter highlights strategies for optimizing data combinations to improve generalization and robustness in robotic manipulation.