To operate safely and efficiently alongside human workers, collaborative robots (cobots) require the ability to quickly understand the dynamics of manipulated objects. However, traditional methods for estimating the full set of inertial parameters rely on motions that are necessarily fast and unsafe (to achieve a sufficient signal-to-noise ratio). In this work, we take an alternative approach: by combining visual and force-torque measurements, we develop an inertial parameter identification algorithm that requires slow or 'stop-and-go' motions only, and hence is ideally tailored for use around humans. Our technique, called Homogeneous Part Segmentation (HPS), leverages the observation that man-made objects are often composed of distinct, homogeneous parts. We combine a surface-based point clustering method with a volumetric shape segmentation algorithm to quickly produce a part-level segmentation of a manipulated object; the segmented representation is then used by HPS to accurately estimate the object's inertial parameters. To benchmark our algorithm, we create and utilize a novel dataset consisting of realistic meshes, segmented point clouds, and inertial parameters for 20 common workshop tools. Finally, we demonstrate the real-world performance and accuracy of HPS by performing an intricate 'hammer balancing act' autonomously and online with a low-cost collaborative robotic arm. Our code and dataset are open source and freely available.

翻译：为在人类工人身边安全高效地协作，协作机器人需要快速理解被操作物体的动力学特性。然而，传统估计全套惯性参数的方法依赖于高速且不安全的运动（以达到足够的信噪比）。在本工作中，我们采用替代方案：通过融合视觉与力-力矩测量，开发了一种仅需低速或“启停式”运动的惯性参数辨识算法，因此特别适合在人类周围使用。我们的技术称为齐次部件分割（HPS），利用了“人造物体常由不同齐次部件构成”的观察。我们将基于表面的点聚类方法与体积形状分割算法相结合，快速生成操作物体的部件级分割；随后HPS利用该分割表示精确估计物体的惯性参数。为评估算法性能，我们创建并使用了包含20种常见车间工具的逼真网格、分割点云及惯性参数的新数据集。最终，我们通过低成本协作机械臂自主在线完成精密的“锤子平衡表演”，验证了HPS在真实场景中的性能与精度。我们的代码与数据集均为开源且免费提供。