迈向用于处理物体姿态不确定性的模块化抓取框架
Towards a Modular Bin-picking Framework for Handling Object Pose Uncertainties
浏览论文内容
中文总结 AI 辅助
本文针对精确抓取应用中物体姿态估计和抓取过程的误差问题，提出模块化框架。通过物体姿态分布估计和第二视角模块降低姿态不确定性，用独立模块补偿抓取误差，经真实场景测试，该框架能提高效率，是解决相关不确定性的首个可互换模块框架。
中文摘要 AI 辅助
近年来，对用于精确抓取应用的强大机器人系统的兴趣日益浓厚。为实现可靠性能，此类系统必须解决物体姿态估计和抓取过程中产生的误差。虽已提出多种方法，但通常针对特定挑战，未提供通用解决方案。本文提出一个联合处理这两种误差类型的模块化框架。该框架纳入物体姿态分布估计以考虑姿态不确定性，这种不确定性常出现在观察模糊、无法确定单个正确姿态的情况。为进一步降低不确定性，引入计算互补姿态分布并随后融合的第二视角模块，融合降低了总体不确定性并提高了系统效率。还包含两个独立模块补偿抓取误差。模块化设计允许组件根据物理设置组合以实现最佳性能或单独使用。该方法在真实场景中对三个不同物体进行评估，无错误发生，所有模块均提高了效率。这些结果表明，将姿态分布与抓取姿态误差相结合是开发更灵活可靠机器人生产系统的一个有前景的方向。据我们所知，这是首个使用可互换模块联合解决抓取和物体姿态不确定性的框架。我们相信有充分机会集成更多模块以提高性能和灵活性。当前框架限于SO(2)中的姿态不确定性，但可扩展到SE(3)，使更多模块改进系统。
英文摘要
In recent years, there has been growing interest in robust robotic systems for precise bin-picking applications. To achieve reliable performance, such systems must address errors arising from both the object pose estimation and the grasping process. Although various approaches have been proposed, they typically target specific challenges and do not offer general solutions. In this paper, we present a modular framework that jointly handles both error types. The framework incorporates object pose distribution estimation to account for pose uncertainty, which frequently arises in situations with ambiguous observations where a single correct pose cannot be determined. To further reduce uncertainty, we introduce a second-viewpoint module that computes complementary pose distributions, which are subsequently fused. This fusion decreases overall uncertainty and improves system efficiency. Additionally, two independent modules are included to compensate for grasping errors. The modular design allows the components to be combined for optimal performance or used individually, depending on the physical setup. The proposed method is evaluated in a real-world setup with three different objects, with no errors, and all modules are shown to improve efficiency. These results suggest that incorporating pose distributions with grasping pose errors is a promising direction for developing more flexible and reliable robotic production systems. To the best of our knowledge, this is the first framework that jointly addresses both grasping and object pose uncertainties using interchangeable modules. We believe there is ample opportunity to integrate additional modules, resulting in improved performance and flexibility. The current framework is limited to pose uncertainties in SO(2), but it could be extended to SE(3), enabling additional modules to improve the system.
