论文标题
元学习重新制定物理不足对象的策略
Meta-Learning Regrasping Strategies for Physical-Agnostic Objects
论文作者
论文摘要
由于未知的物理特性（例如质量分布和摩擦系数），在现实世界应用中抓住不均匀的对象仍然是一项具有挑战性的任务。在这项研究中，我们提出了一种称为cuntex的元学习算法，该算法将有条件的神经过程（CNP）与dexnet-2.0结合在一起，以自主使用深度图像的物理特性自主辨别物理特性。 Cundex有效地从有限试验中获取了物理嵌入，从而实现了精确的握把估计。此外，Cundex能够以在线方式从新试验中更新预测的质量迭代。据我们所知，我们是第一个生成两个对象数据集的人，这些对象数据集专注于具有不同质量分布和摩擦系数的不均匀物理特性。在模拟中进行了广泛的评估表明，Cundex优于Dexnet-2.0和现有的基于元学习的握把管道。此外，尽管仅在模拟中训练，但Cundex仍显示出对以前看不见的现实对象的强大概括。合成和现实数据集也将发布。
Grasping inhomogeneous objects in real-world applications remains a challenging task due to the unknown physical properties such as mass distribution and coefficient of friction. In this study, we propose a meta-learning algorithm called ConDex, which incorporates Conditional Neural Processes (CNP) with DexNet-2.0 to autonomously discern the underlying physical properties of objects using depth images. ConDex efficiently acquires physical embeddings from limited trials, enabling precise grasping point estimation. Furthermore, ConDex is capable of updating the predicted grasping quality iteratively from new trials in an online fashion. To the best of our knowledge, we are the first who generate two object datasets focusing on inhomogeneous physical properties with varying mass distributions and friction coefficients. Extensive evaluations in simulation demonstrate ConDex's superior performance over DexNet-2.0 and existing meta-learning-based grasping pipelines. Furthermore, ConDex shows robust generalization to previously unseen real-world objects despite training solely in the simulation. The synthetic and real-world datasets will be published as well.
