李弘扬老师团队最新工作X0！超低成本高效实现机器人操作任务~

Core Insights - The article emphasizes the importance of achieving 100% reliability in robotic manipulation tasks through a strategic approach rather than merely increasing data scale [2][4]. Methodology - The proposed methodology consists of three stages: data collection, model training, and real-world inference, which are interconnected and critical for success [2]. - The approach focuses on pattern consistency, model algorithms, and leveraging phase advantages to optimize the transition from perception to action [3]. Pattern Consistency - The article defines the effective action distribution for specific tasks and highlights the need for dynamic alignment among human demonstration, learned strategies, and real-world execution [8][10]. - It identifies potential inconsistencies in traditional imitation learning processes, such as distribution shifts and deployment biases, which can lead to task failures [11][12]. Model Algorithms - The introduction of the Model Arithmetic (MA) method allows for training on new data subsets and merging models without the high costs associated with retraining on full datasets [27][30]. - The MA method successfully integrates different learned manifolds, enhancing model performance beyond that of models trained on full datasets [30]. Phase Advantages - The article discusses the significance of estimating advantage signals directly as a modeling objective, which improves the reliability of state transitions during long-horizon tasks [31][35]. - The proposed Direct+Stage method enhances the stability and smoothness of progress accumulation in robotic tasks [37]. Performance Improvements - Enhanced data collection methods and online strategy recovery trajectories significantly improve model recovery capabilities, leading to higher success rates and reduced retry costs [21]. - The implementation of spatiotemporal enhancements has resulted in increased throughput and task completion rates [23][26]. Conclusion - The article concludes that not all robotic data holds equal value, and the ability to quickly evaluate and select high-quality foundational strategies is crucial for effective research iterations [41]. - The findings suggest that re-evaluating fundamental concepts in reinforcement learning could yield further benefits in robotic manipulation tasks [41].