💡 The paper proposes a data pyramid framework for embodied manipulation, which organizes data sources into five complementary categories: real-robot data, UMl-style data, egocentric and exocentric data, simulation data, and general vision-language data. The framework is designed to address the challenges of embodied agents, which require data that couples observations with physical states and actions. The authors analyze recent embodied foundation models through the lens of their data recipes, examining how different sources are selected, aligned, and mixed during training. They relate data composition to capabilities in perception, reasoning, planning, action generation, and world prediction. The paper also discusses six open challenges, including building large-scale tactile datasets, collecting failure and recovery data, developing scalable data-collection pipelines, aligning actions across embodiments, leveraging egocentric data for dexterous manipulation, and designing principled data recipes for robot learning. The proposed data pyramid framework provides a foundation for the design of next-generation embodied systems, which can learn to perceive, reason, and act in complex environments. The authors hope that this work will pave the way for the development of more advanced embodied agents that can learn from diverse data sources and improve their performance in various tasks.