The Samsung robotics team has introduced a novel method called ε4P, designed to address the challenge of utilizing imperfect data in the learning process for fine robotic manipulation. Traditionally, a large amount of imperfect data, when directly mixed for training, tends to degrade model performance and is therefore often discarded. The ε4P method categorizes imperfect data based on the differences in action and task context dependency across different noise intervals during flow matching training: data that matches the task but lacks precision is used in high-noise intervals to provide correct task context; data that matches in precision but not in task is used in low-noise intervals to provide precise action supervision. This approach fully unlocks the value of imperfect data without altering the model architecture, training objectives, or data sampling probabilities. Multiple real-world tests demonstrate that, when using the same dataset, ε4P significantly improves task success rates compared to direct mixed training, outperforming baselines that rely solely on high-precision data from the target task. The study also reveals that data deemed harmful during direct mixed training may not necessarily have low potential value. Rather than adjusting data weights, it is more crucial to clarify the scenarios where data can make a meaningful contribution. Utilizing imperfect data with the ε4P method can achieve results comparable to adding an equivalent amount of high-precision data, potentially reducing the reliance on specially collected high-precision demonstration data for robotic manipulation tasks and thereby lowering the cost of demonstration collection.