  The pursuit of artificial general intelligence (AGI) has been accelerated by
Multimodal Large Language Models (MLLMs), which exhibit superior reasoning,
generalization capabilities, and proficiency in processing multimodal inputs. A
crucial milestone in the evolution of AGI is the attainment of human-level
planning, a fundamental ability for making informed decisions in complex
environments, and solving a wide range of real-world problems. Despite the
impressive advancements in MLLMs, a question remains: How far are current MLLMs
from achieving human-level planning? To shed light on this question, we
introduce EgoPlan-Bench, a comprehensive benchmark to evaluate the planning
abilities of MLLMs in real-world scenarios from an egocentric perspective,
mirroring human perception. EgoPlan-Bench emphasizes the evaluation of planning
capabilities of MLLMs, featuring realistic tasks, diverse action plans, and
intricate visual observations. Our rigorous evaluation of a wide range of MLLMs
reveals that EgoPlan-Bench poses significant challenges, highlighting a
substantial scope for improvement in MLLMs to achieve human-level task
planning. To facilitate this advancement, we further present EgoPlan-IT, a
specialized instruction-tuning dataset that effectively enhances model
performance on EgoPlan-Bench. We have made all codes, data, and a maintained
benchmark leaderboard available to advance future research.
