FreeAskWorld: An Interactive and Closed-Loop Simulator for Human-Centric Embodied AI

TL;DR

FreeAskWorld is a comprehensive, human-centric simulation platform that integrates language models and social cognition to enhance embodied AI's interaction and planning capabilities, supported by a large-scale, annotated benchmark dataset.

Contribution

The paper introduces FreeAskWorld, a novel interactive simulation framework with a new benchmark dataset for training and evaluating socially aware embodied AI systems.

Findings

Models fine-tuned on FreeAskWorld outperform original models.

The framework improves semantic understanding and interaction skills.

Interaction acts as an additional information modality.

Abstract

As embodied intelligence emerges as a core frontier in artificial intelligence research, simulation platforms must evolve beyond low-level physical interactions to capture complex, human-centered social behaviors. We introduce FreeAskWorld, an interactive simulation framework that integrates large language models (LLMs) for high-level behavior planning and semantically grounded interaction, informed by theories of intention and social cognition. Our framework supports scalable, realistic human-agent simulations and includes a modular data generation pipeline tailored for diverse embodied tasks. To validate the framework, we extend the classic Vision-and-Language Navigation (VLN) task into a interaction enriched Direction Inquiry setting, wherein agents can actively seek and interpret navigational guidance. We present and publicly release FreeAskWorld, a large-scale benchmark dataset comprising reconstructed environments, six diverse task types, 16 core object categories, 63,429 annotated sample frames, and more than 17 hours of interaction data to support training and evaluation of embodied AI systems. We benchmark VLN models, and human participants under both open-loop and closed-loop settings. Experimental results demonstrate that models fine-tuned on FreeAskWorld outperform their original counterparts, achieving enhanced semantic understanding and interaction competency. These findings underscore the efficacy of socially grounded simulation frameworks in advancing embodied AI systems toward sophisticated high-level planning and more naturalistic human-agent interaction. Importantly, our work underscores that interaction itself serves as an additional information modality.