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}{this https URL}.

利用四足机器人狗作为自主代理解决室内外不同环境中的垃圾管理和回收挑战，通过废物分割和属性分析方法，准确确定垃圾的状态，提高夹取能力，并通过包含 GSA2D 数据集的广泛实验，全面分析 GSA2Seg 的有效性。