FreeAskWorld：交互式具身闭环仿真框架

Core Insights - The article discusses the limitations of existing Visual-Language Navigation (VLN) solutions in the field of embodied intelligence, highlighting issues such as reliance on static instructions, lack of social interaction capabilities, and inadequate simulation environments [1][2] - FreeAskWorld, developed by Tsinghua University's AI Research Institute, introduces an innovative approach combining LLM-driven interactive simulation with Direction Inquiry Tasks to overcome these challenges, achieving social, dynamic, and realistic embodied navigation and interaction [1][2][4] Summary by Sections Current Challenges in VLN - Existing VLN solutions face a "triple dilemma": reliance on static instructions, lack of social cognition, and single-dimensional simulation environments [2] - Key deficiencies include inability to handle dynamic scenes, lack of social interaction, and insufficient realism in navigation environments [2] FreeAskWorld's Approach - FreeAskWorld leverages LLM to create realistic social scenarios and employs a closed-loop interaction framework to facilitate dynamic adaptation [2][5] - The system consists of three core components: LLM-driven human simulation, Direction Inquiry Tasks, and a multi-modal dataset [5][8] Core Components - Human Simulation Module: Generates diverse human behaviors that adhere to social rules, enhancing interaction realism [5][7] - Direction Inquiry Task: Allows robots to actively seek help during navigation, improving performance through multi-round interactions [5][7] - Data Generation: The dataset includes 63,429 annotated frames and over 17 hours of interaction data, covering both indoor and outdoor mixed scenes [8][11] Experimental Results - FreeAskWorld demonstrates significant performance improvements in both open-loop and closed-loop settings compared to traditional models, with interaction enhancing navigation success rates [13][14] - The model's ability to adapt to complex environments through social interaction is validated, showing a marked increase in navigation success rates when robots can ask for help [16][19] Future Directions - The article suggests expanding the model's capabilities to support more complex social tasks and integrating additional sensory modalities to enhance adaptability in challenging environments [19][17] - Emphasis is placed on the importance of dynamic environments, human realism, and continuous navigation in future developments [19][17]