  In this technical report, we present our solution for the EgoPlan Challenge
in ICML 2024. To address the real-world egocentric task planning problem, we
introduce a novel planning framework which comprises three stages: long-term
memory Extraction, context-awared Planning, and multi-iteration Decision, named
EPD. Given the task goal, task progress, and current observation, the
extraction model first extracts task-relevant memory information from the
progress video, transforming the complex long video into summarized memory
information. The planning model then combines the context of the memory
information with fine-grained visual information from the current observation
to predict the next action. Finally, through multi-iteration decision-making,
the decision model comprehensively understands the task situation and current
state to make the most realistic planning decision. On the EgoPlan-Test set,
EPD achieves a planning accuracy of 53.85% over 1,584 egocentric task planning
questions. We have made all codes available at https://github.com/Kkskkkskr/EPD .
