Differentiable simulation for physical system identification
| Autor: | Igor Kalevatykh, Cordelia Schmid, Quentin Le Lidec, Justin Carpentier, Ivan Laptev |
|---|---|
| Přispěvatelé: | Models of visual object recognition and scene understanding (WILLOW), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Département d'informatique - ENS Paris (DI-ENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Service Expérimentation et Développement [Paris] (SED), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), This work was supported in part by the HPC resources from GENCI-IDRIS(Grant AD011011342), the French government under management of Agence Nationale de la Recherche as part of the 'Investissements d’avenir' program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute), andLouis Vuitton ENS Chair on Artificial Intelligence., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Département d'informatique de l'École normale supérieure (DI-ENS), Centre National de la Recherche Scientifique (CNRS), Université Paris sciences et lettres (PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization Control and Optimization Optimization problem Simulation and Animation Biomedical Engineering Physical system 02 engineering and technology 010501 environmental sciences 01 natural sciences [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 020901 industrial engineering & automation Artificial Intelligence Reinforcement learning [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] Nonlinear complementarity problem Differentiable function 0105 earth and related environmental sciences Mechanical Engineering Optimization and Optimal Control Approximation algorithm Calibration and Identification Optimal control Linear complementarity problem Computer Science Applications Human-Computer Interaction Control and Systems Engineering Computer Vision and Pattern Recognition [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] Contact Modeling |
| Zdroj: | IEEE Robotics and Automation Letters IEEE Robotics and Automation Letters, IEEE 2021, 6 (2), pp.3413-3420. ⟨10.1109/LRA.2021.3062323⟩ IEEE Robotics and Automation Letters, IEEE 2021 IEEE Robotics and Automation Letters, 2021, 6 (2), pp.3413-3420. ⟨10.1109/LRA.2021.3062323⟩ |
| ISSN: | 2377-3766 |
| DOI: | 10.1109/LRA.2021.3062323⟩ |
| Popis: | International audience; Simulating frictional contacts remains a challenging research topic in robotics. Recently, differentiable physics emerged and has proven to be a key element in modelbased Reinforcement Learning (RL) and optimal control fields. However, most of the current formulations deploy coarse approximations of the underlying physical principles. Indeed, the classic simulators lose precision by casting the Nonlinear Complementarity Problem (NCP) of frictional contact into a Linear Complementarity Problem (LCP) to simplify computations. Moreover, such methods deploy non-smooth operations and cannot be automatically differentiated. In this paper, we propose (i) an extension of the staggered projections algorithm for more accurate solutions of the problem of contacts with friction. Based on this formulation, we introduce (ii) a differentiable simulator and an efficient way to compute the analytical derivatives of the involved optimization problems. Finally, (iii) we validate the proposed framework with a set of experiments to present a possible application of our differentiable simulator. In particular, using our approach we demonstrate accurate estimation of friction coefficients and object masses both in synthetic and real experiments. |
| Databáze: | OpenAIRE |
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