Newtonian and Lagrangian Neural Networks: A Comparison Towards Efficient Inverse Dynamics Identification
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Abstract (arXiv)
Accurate inverse dynamics models are essential tools for controlling industrial robots. Recent research combines neural network regression with inverse dynamics formulations of the Newton-Euler and the Euler-Lagrange equations of motion, resulting in so-called Newtonian neural networks and Lagrangian neural networks, respectively. These physics-informed models seek to identify unknowns in the analytical equations from data. Despite their potential, current literature lacks guidance on choosing between Lagrangian and Newtonian networks. In this study, we show that when motor torques are estimated instead of directly measuring joint torques, Lagrangian networks prove less effective compared to Newtonian networks as they do not explicitly model dissipative torques. The performance of these models is compared to neural network regression on data of a MABI MAX 100 industrial robot.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 18 of its 39 references that are in the map. First posted on arXiv 22 Jun 2025.
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18 of 39 references.
References · 18
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2019Hamiltonian Neural Networkscited by 712
- 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 608
- 2019Learning agile and dynamic motor skills for legged robotscited by 504
- 2020Lagrangian Neural Networkscited by 398
- 2019Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learningcited by 212
- 2020Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraintscited by 105
- 2021Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modelingcited by 41
- 2019Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systemscited by 33
- 2020Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learningcited by 27
- 2020Encoding Physical Constraints in Differentiable Newton-Euler Algorithmcited by 23
- 2020Structured Mechanical Models for Robot Learning and Controlcited by 21
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- 2021Combining Physics and Deep Learning to learn Continuous-Time Dynamics Modelscited by 20
- 2020Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processescited by 13
- 2024Generalized Lagrangian Neural Networkscited by 10
- 2016Identification of Fully Physical Consistent Inertial Parameters using Optimization on Manifoldscited by 8
- 2020Structured learning of rigid-body dynamics: A survey and unified view from a robotics perspectivecited by 7
- 2020A Differentiable Newton Euler Algorithm for Multi-body Model Learningcited by 6
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Citing papers per year: 2026: 1.
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