arXiv:2606.19948v1 Announce Type: new Abstract: For embodied agents capable of physical interaction, the capability to create and understand dialog is crucial to ensure both safety and effectiveness. While DialNav~\cite{han2025dialnav} provides a framework for holistic evaluation of the dialog--execution loop in photorealistic indoor navigation, its performance remains limited by a critical scarcity of training data (2K episodes). To address this, we propose an automatic generation pipeline, and construct the \textbf{RAINbow} dataset, a large-scale training dataset with 238K episodes for DialN
The rapid advancement of large language models and embodied AI research necessitates robust data generation techniques to overcome critical training data limitations.
Improving the ability of embodied agents to understand and generate dialog in real-world environments is crucial for their effective and safe deployment across various applications.
The introduction of automated data generation pipelines for embodied dialog significantly expands the scale and quality of training data available, accelerating development in this field.
- · AI research labs
- · Embodied AI developers
- · Robotics companies
- · Manual data annotation services
Embodied agents develop more sophisticated and natural conversational and navigation capabilities.
Faster deployment of AI agents in complex interactive environments, from smart homes to industrial settings.
Enhanced human-robot collaboration across various sectors, improving efficiency and accessibility.
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Read at arXiv cs.AI