TR2023-092
Forward Dynamics Estimation from Data-Driven Inverse Dynamics Learning
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- "Forward Dynamics Estimation from Data-Driven Inverse Dynamics Learning", World Congress of the International Federation of Automatic Control (IFAC), DOI: 10.1016/j.ifacol.2023.10.1620, July 2023, pp. Pages 519-524.BibTeX TR2023-092 PDF
- @inproceedings{DallaLibera2023jul2,
- author = {Dalla Libera, Alberto and Giacomuzzo, Giulio and Carli, Ruggero and Nikovski, Daniel and Romeres, Diego},
- title = {Forward Dynamics Estimation from Data-Driven Inverse Dynamics Learning},
- booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
- year = 2023,
- pages = {Pages 519--524},
- month = jul,
- doi = {10.1016/j.ifacol.2023.10.1620},
- url = {https://www.merl.com/publications/TR2023-092}
- }
,
- "Forward Dynamics Estimation from Data-Driven Inverse Dynamics Learning", World Congress of the International Federation of Automatic Control (IFAC), DOI: 10.1016/j.ifacol.2023.10.1620, July 2023, pp. Pages 519-524.
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MERL Contacts:
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Research Areas:
Abstract:
In this paper, we propose to estimate the forward dynamics equations of mechanical systems by learning a model of the inverse dynamics and estimating individual dynamics components from it. We revisit the classical formulation of rigid body dynamics in order to extrapolate the physical dynamical components, such as inertial and gravitational components, from an inverse dynamics model. After estimating the dynamical components, the forward dynamics can be computed in closed form as a function of the learned inverse dynamics. We tested the proposed method with several machine learning models based on Gaussian Process Regression and compared them with the standard approach of learning the forward dynamics directly. Results on two simulated robotic manipulators, a PANDA Franka Emika and a UR10, show the effectiveness of the proposed method in learning the forward dynamics, both in terms of accuracy as well as in opening the possibility of using more structured models.
Related News & Events
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NEWS MERL presents 9 papers at 2023 IFAC World Congress Date: July 9, 2023 - July 14, 2023
MERL Contacts: Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Christopher R. Laughman; Diego Romeres; Abraham P. Vinod
Research Areas: Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, RoboticsBrief- MERL researchers presented 9 papers and organized 2 invited/workshop sessions at the 2023 IFAC World Congress held in Yokohama, JP.
MERL's contributions covered topics including decision-making for autonomous vehicles, statistical and learning-based estimation for GNSS and energy systems, impedance control for delta robots, learning for system identification of rigid body dynamics and time-varying systems, and meta-learning for deep state-space modeling using data from similar systems. The invited session (MERL co-organizer: Ankush Chakrabarty) was on the topic of “Estimation and observer design: theory and applications” and the workshop (MERL co-organizer: Karl Berntorp) was on “Gaussian Process Learning for Systems and Control”.
- MERL researchers presented 9 papers and organized 2 invited/workshop sessions at the 2023 IFAC World Congress held in Yokohama, JP.
Related Publication
- @article{DallaLibera2023jul,
- author = {Dalla Libera, Alberto and Giacomuzzo, Giulio and Carli, Ruggero and Nikovski, Daniel and Romeres, Diego},
- title = {Forward Dynamics Estimation from Data-Driven Inverse Dynamics Learning},
- journal = {arXiv},
- year = 2023,
- month = jul,
- url = {https://arxiv.org/abs/2307.05093}
- }