Identification of Fully Physical Consistent Inertial Parameters using Optimization on Manifolds
Not on the map. Why?
Abstract (arXiv)
This paper presents a new condition, the fully physical consistency for a set of inertial parameters to determine if they can be generated by a physical rigid body. The proposed condition ensure both the positive definiteness and the triangular inequality of 3D inertia matrices as opposed to existing techniques in which the triangular inequality constraint is ignored. This paper presents also a new parametrization that naturally ensures that the inertial parameters are fully physical consistency. The proposed parametrization is exploited to reformulate the inertial identification problem as a manifold optimization problem, that ensures that the identified parameters can always be generated by a physical body. The proposed optimization problem has been validated with a set of experiments on the iCub humanoid robot.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Oct 2016.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2020: 1; 2021: 1; 2022: 1; 2023: 2; 2024: 1; 2025: 2.
8 papers.
- 2020Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learningcited by 27
- 2021Combining Physics and Deep Learning to learn Continuous-Time Dynamics Modelscited by 20
- 2024Morphological Symmetries in Roboticscited by 13
- 2022End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Controlcited by 2
- 2023The Sum of Its Parts: Visual Part Segmentation for Inertial Parameter Identification of Manipulated Objectscited by 2
- 2023A Structured Prediction Approach for Robot Imitation Learningcited by 1
- 2025Newtonian and Lagrangian Neural Networks: A Comparison Towards Efficient Inverse Dynamics Identificationcited by 1
- 2025Diff-MSM: Differentiable MusculoSkeletal Model for Simultaneous Identification of Human Muscle and Bone Parameterscited by 0
- 2025Diff-MSM: Differentiable MusculoSkeletal Model for Simultaneous Identification of Human Muscle and Bone Parameters18 Aug 2025
- 2025Newtonian and Lagrangian Neural Networks: A Comparison Towards Efficient Inverse Dynamics Identification22 Jun 2025
- 2024Morphological Symmetries in Robotics23 Feb 2024
- 2023A Structured Prediction Approach for Robot Imitation Learning26 Sep 2023
- 2023The Sum of Its Parts: Visual Part Segmentation for Inertial Parameter Identification of Manipulated Objects13 Feb 2023
- 2022End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control27 May 2022
- 2021Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models5 Oct 2021
- 2020Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning3 Nov 2020