Yann De Mont-Marin
Research Subjects
Yann De Mont-Marin has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
Ajay Sathya
1 shared publication- Ajay Suresha Sathya
Inria - Dpartement dInformatique de lcole normale suprieure, PSL Research University, Paris, France
1 shared publication - Justin Carpentier
Inria and Dpartement dInformatique de lcole Normale Suprieure, PSL Research University in Paris, Paris, France
1 shared publication - Louis Montaut
Inria and Dpartement dInformatique de lcole Normale Suprieure, PSL Research University in Paris, Paris, France
1 shared publication
Latest Publications
- Open Access
Matrix-Free Delassus Operations: Scalable and Memory-Efficient Algorithms
The Delassus matrix, closely related to the operational-space inertia matrix, is a fundamental quantity in robotics with applications in simulation, system identification, and control. Traditional approaches compute and store this matrix explicitly, either in sparse or dense form. In this work, we depart from this convention by treating the Delassus matrix as a matrix-free operator. We derive efficient algorithms with low computational complexity that multiply the Delassus matrix or its damped inverse by an input vector or matrix. Unlike approaches based on explicit matrix construction, our method achieves a linear memory footprint, making it scalable to problems with thousands of constraints and suitable for execution on resourcelimited hardware. We implement these matrix-free operations on top of the open-source Pinocchio library and evaluate their performance against state-of-the-art methods that rely on explicit matrix computation. Our benchmarks demonstrate substantial speedups, ranging from 2x to over 400x, in contact-rich scenarios.
Open AccessMatrix-Free Delassus Operations: Scalable and Memory-Efficient Algorithms
The Delassus matrix, closely related to the operational-space inertia matrix, is a fundamental quantity in robotics with applications in simulation, system identification, and control. Traditional approaches compute and store this matrix explicitly, either in sparse or dense form. In this work, we depart from this convention by treating the Delassus matrix as a matrix-free operator. We derive efficient algorithms with low computational complexity that multiply the Delassus matrix or its damped inverse by an input vector or matrix. Unlike approaches based on explicit matrix construction, our method achieves a linear memory footprint, making it scalable to problems with thousands of constraints and suitable for execution on resourcelimited hardware. We implement these matrix-free operations on top of the open-source Pinocchio library and evaluate their performance against state-of-the-art methods that rely on explicit matrix computation. Our benchmarks demonstrate substantial speedups, ranging from 2x to over 400x, in contact-rich scenarios.