Incremental Semiparametric Inverse Dynamics Learning
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Abstract (arXiv)
This paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonparametric modeling based on incremental kernel methods, with no prior information on the mechanical properties of the system. This yields to an incremental semiparametric approach, leveraging the advantages of both the parametric and nonparametric models. We validate the proposed technique learning the dynamics of one arm of the iCub humanoid robot.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jan 2016.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1.
3 papers.
- 2021Combining Physics and Deep Learning to learn Continuous-Time Dynamics Modelscited by 20
- 2023A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identificationcited by 4
- 2024Enhanced Prediction of Multi-Agent Trajectories via Control Inference and State-Space Dynamicscited by 2
- 2024Enhanced Prediction of Multi-Agent Trajectories via Control Inference and State-Space Dynamics8 Aug 2024
- 2023A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification10 Oct 2023
- 2021Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models5 Oct 2021