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.

翻译：现实应用中抓取非均匀物体仍是一项具有挑战性的任务，这是由于质量分布和摩擦系数等未知物理属性所致。本研究提出了一种名为ConDex的元学习算法，该算法将条件神经过程与DexNet-2.0相结合，通过深度图像自主辨识物体的潜在物理属性。ConDex能够从有限的试错中高效获取物理嵌入，从而实现精确的抓取点估计。此外，ConDex还可基于新试错以在线方式迭代更新预测的抓取质量。据我们所知，我们是首个构建两个分别聚焦于具有不同质量分布和摩擦系数的非均匀物理属性物体数据集的研究团队。仿真环境中的大量评估表明，ConDex的性能优于DexNet-2.0及现有基于元学习的抓取流程。此外，尽管仅在仿真环境中训练，ConDex仍展现出对未见真实世界物体的鲁棒泛化能力。本文同时将公开合成数据集与真实世界数据集。