SweepMM: A High-Quality Multimodal Dataset for Sweeping Robots in Home Scenarios for Vision-Language Model

Abstract

Embodied intelligence based on vision-language models aims to learn from interactions and derive general intelligence. However, existing generalized vision-language models cannot understand domain knowledge in home scenarios due to the lack of sweeping robot multimodal datasets. In this paper, we propose the first multimodal dataset for sweeping robots, called SweepMM. We create textual data such as room type, scene descriptions, and moving recommendations using various approaches including rule-based, manual-based, and off-the-shelf model-based methods. Based on this dataset, we fine-tune the first generative pretrained model for sweeping robots, called SweepGPM. This model enables human-robot dialogue and surpasses previous state-of-the-art methods by 0.8% in room type recognition, 0.4% in obstacle detection, and 8.0% in lost item search, demonstrating the potential of embodied intelligence in sweeping robots.