# The Sum of Its Parts: Visual Part Segmentation for Inertial Parameter Identification of Manipulated Objects

arXiv: https://arxiv.org/abs/2302.06685 · HTML: https://mlsanity.com/paper/2302.06685/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 13 Feb 2023.

## Builds on (most cited first, 8 of 8)

- [Point2Skeleton: Learning Skeletal Representations from Point Clouds](https://mlsanity.com/paper/2012.00230/) (2020)
- [Coverage Axis: Inner Point Selection for 3D Shape Skeletonization](https://mlsanity.com/paper/2110.00965/) (2021)
- [Linear Matrix Inequalities for Physically-Consistent Inertial Parameter Identification: A Statistical Perspective on the Mass Distribution](https://mlsanity.com/paper/1701.04395/) (2017)
- [Identification of Fully Physical Consistent Inertial Parameters using Optimization on Manifolds](https://mlsanity.com/paper/1610.08703/) (2016)
- [SEG-MAT: 3D Shape Segmentation Using Medial Axis Transform](https://mlsanity.com/paper/2010.11488/) (2020)
- [In-Hand Object-Dynamics Inference using Tactile Fingertips](https://mlsanity.com/paper/2003.13165/) (2020)
- [A Probabilistic Model for Planar Sliding of Objects with Unknown Material Properties: Identification and Robust Planning](https://mlsanity.com/paper/2008.01921/) (2020)
- [Fast Object Inertial Parameter Identification for Collaborative Robots](https://mlsanity.com/paper/2203.00830/) (2022)

## Built on by (most cited first, 2 of 2)

- [3x2: 3D Object Part Segmentation by 2D Semantic Correspondences](https://mlsanity.com/paper/2407.09648/) (2024)
- [Stable Object Placement Planning From Contact Point Robustness](https://mlsanity.com/paper/2410.12483/) (2024)

## Abstract (arXiv)

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.