论文标题
Lasesom：软对象操纵的潜在和语义表示框架
LaSeSOM: A Latent and Semantic Representation Framework for Soft Object Manipulation
论文作者
论文摘要
由于其在许多经济重要的领域中的潜在应用，软件操纵最近在机器人界的社区中广受欢迎。尽管最近在这些类型的任务中取得了巨大进展，但大多数最先进的方法都是特定于案例的。它们只能用于执行单个变形任务（例如弯曲），因为它们的形状表示算法通常依赖于“硬编码”功能。在本文中，我们提出了Lassom，这是一种用于语义软对象操纵的新反馈潜在框架。我们的新方法在低级几何特征提取和高级语义形状分析之间引入了内部潜在表示层。这允许识别每个压缩语义函数，并从不同的特征提取级别形成有效的形状分类器。提出的潜在框架使软对象表示更通用（独立于对象的几何形状及其机械属性），并且可扩展（它可以与1D/2D/3D任务一起使用）。它的高级语义层使得（准）具有软对象的形状计划任务，这是许多软操作任务中有价值且毫无疑问的功能。为了验证这种新方法，我们报告了一项针对机器人操纵剂的详细实验研究。
Soft object manipulation has recently gained popularity within the robotics community due to its potential applications in many economically important areas. Although great progress has been recently achieved in these types of tasks, most state-of-the-art methods are case-specific; They can only be used to perform a single deformation task (e.g. bending), as their shape representation algorithms typically rely on "hard-coded" features. In this paper, we present LaSeSOM, a new feedback latent representation framework for semantic soft object manipulation. Our new method introduces internal latent representation layers between low-level geometric feature extraction and high-level semantic shape analysis; This allows the identification of each compressed semantic function and the formation of a valid shape classifier from different feature extraction levels. The proposed latent framework makes soft object representation more generic (independent from the object's geometry and its mechanical properties) and scalable (it can work with 1D/2D/3D tasks). Its high-level semantic layer enables to perform (quasi) shape planning tasks with soft objects, a valuable and underexplored capability in many soft manipulation tasks. To validate this new methodology, we report a detailed experimental study with robotic manipulators.
