Efficient waste management and recycling heavily rely on garbage exploration and identification. In this study, we propose GSA2Seg (Garbage Segmentation and Attribute Analysis), a novel visual approach that utilizes quadruped robotic dogs as autonomous agents to address waste management and recycling challenges in diverse indoor and outdoor environments. Equipped with advanced visual perception system, including visual sensors and instance segmentators, the robotic dogs adeptly navigate their surroundings, diligently searching for common garbage items. Inspired by open-vocabulary algorithms, we introduce an innovative method for object attribute analysis. By combining garbage segmentation and attribute analysis techniques, the robotic dogs accurately determine the state of the trash, including its position and placement properties. This information enhances the robotic arm's grasping capabilities, facilitating successful garbage retrieval. Additionally, we contribute an image dataset, named GSA2D, to support evaluation. Through extensive experiments on GSA2D, this paper provides a comprehensive analysis of GSA2Seg's effectiveness. Dataset available: \href{https://www.kaggle.com/datasets/hellob/gsa2d-2024}{https://www.kaggle.com/datasets/hellob/gsa2d-2024}.

翻译：高效的废物管理与回收严重依赖于垃圾的探索与识别。本研究提出GSA2Seg（垃圾分割与属性分析），一种利用四足机器狗作为自主代理的新型视觉方法，旨在应对多样室内外环境中的废物管理与回收挑战。配备包括视觉传感器与实例分割器在内的先进视觉感知系统后，机器狗能够灵活导航周围环境，勤勉搜寻常见垃圾物品。受开放词汇算法启发，我们引入一种创新的物体属性分析方法。通过结合垃圾分割与属性分析技术，机器狗可准确判定垃圾状态，包括其位置与放置属性。该信息增强了机器臂的抓取能力，有助于成功回收垃圾。此外，我们贡献了一个名为GSA2D的图像数据集以支持评估。通过在GSA2D上的大量实验，本文全面分析了GSA2Seg的有效性。数据集地址：\href{https://www.kaggle.com/datasets/hellob/gsa2d-2024}{https://www.kaggle.com/datasets/hellob/gsa2d-2024}。