Core Insights - The article discusses the limitations of current robot foundational models that primarily rely on demonstration data, highlighting the need for a structured reinforcement learning (RL) framework called Recap to enhance robot performance and reliability [2][3][10]. Group 1: Limitations of Current Models - Current models depend heavily on demonstration data, which incurs high human costs and limits the strategies to human-level performance, lacking self-improvement capabilities [2][10]. - The article emphasizes that merely increasing model size is insufficient; a restructured training paradigm is essential for robots to transition from "can demonstrate" to "can deploy at scale" [3][10]. Group 2: Introduction of Recap Framework - Recap integrates three training phases: demonstration, correction, and robot autonomous rollouts, allowing for continuous improvement in strategy quality [2][10]. - The framework addresses the compounding error problem in robot strategies by systematically utilizing correction data, value functions, and advantages [3][10][12]. Group 3: Performance of π*(0.6) Model - The π*(0.6) model, with 5 billion parameters, demonstrates the ability to handle heterogeneous prompts and achieve performance thresholds suitable for commercial deployment [3][20]. - The model shows significant improvements in task execution, achieving over 90% success rates in complex tasks such as making espresso, folding clothes, and assembling boxes [25][20]. Group 4: Learning Process and Challenges - The learning process involves three stages: offline reinforcement learning pre-training, task-specific fine-tuning, and continuous improvement through real-world experience [19][20]. - The article outlines the challenges faced in high-throughput, autonomous execution, particularly in tasks requiring complex physical operations and adaptability to various conditions [24][20]. Group 5: Data Sources for Learning - The article identifies three data sources for robot learning: expert demonstrations for defining new behaviors, guidance for refining strategies, and autonomous experience for behavior enhancement [27][28]. - It posits that autonomous experience may become a crucial data source as robots are deployed more widely in real-world applications, potentially enabling performance that surpasses human capabilities [27][28].