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：为与人类工人安全高效地协同工作，协作机器人需具备快速理解所操控物体动力学特性的能力。然而，传统方法估算完整惯性参数集依赖于高速且不安全的运动（为获取足够信噪比）。本研究另辟蹊径：通过融合视觉与力/扭矩测量，我们开发出一种仅需低速"启停"运动的惯性参数辨识算法，因此非常适合人机协作场景。我们提出的同质部件分割（Homogeneous Part Segmentation, HPS）技术，利用人造物体常由不同均质部件构成的观测事实，将基于表面的点聚类方法与体素形状分割算法相结合，快速生成操控物体的部件级分割表示；进而通过HPS基于该分割表示精确估计物体惯性参数。为评估算法性能，我们构建并发布了一个包含20种常见车间工具的真实网格模型、分割点云及惯性参数的新颖数据集。最后，通过低成本的协作机械臂自主在线完成精妙的"锤子平衡"实验，我们验证了HPS在真实场景中的性能与精度。本文代码与数据集均为开源免费提供。