Learning Robust Task Priorities and Gains for Control of Redundant Robots
| Autor: | Valerio Modugno, Waldez Gomes, Jean-Baptiste Mouret, Luigi Penco, Serena Ivaldi, Enrico Mingo Hoffman |
|---|---|
| Přispěvatelé: | Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment (LARSEN), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Istituto Italiano di Tecnologia (IIT), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 731540(project AnDy) and from the European Research Council (ERC) under Grant Agreement No. 637972 (project ResiBots)., Creativ'Lab, European Project: 731540,H2020,An.Dy(2017), European Project: 637972,H2020 ERC,ERC-2014-STG,ResiBots(2015), Sapienza University [Rome], Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0303 health sciences
0209 industrial biotechnology Control and Optimization Computer science Mechanical Engineering Biomedical Engineering Control engineering 02 engineering and technology Computer Science Applications [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Human-Computer Interaction 03 medical and health sciences 020901 industrial engineering & automation [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] Artificial Intelligence Control and Systems Engineering Control theory Robustness (computer science) Robot [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] Computer Vision and Pattern Recognition Quadratic programming iCub Humanoid robot 030304 developmental biology |
| Zdroj: | IEEE Robotics and Automation Letters IEEE Robotics and Automation Letters, 2020, 5 (2), pp.2626-2633. ⟨10.1109/LRA.2020.2972847⟩ IEEE Robotics and Automation Letters, IEEE 2020, 5 (2), pp.2626-2633. ⟨10.1109/LRA.2020.2972847⟩ |
| ISSN: | 2377-3766 |
| DOI: | 10.1109/LRA.2020.2972847⟩ |
| Popis: | International audience; Generating complex movements in redundant robots like humanoids is usually done by means of multi-task controllers based on quadratic programming, where a multitude of tasks is organized according to strict or soft priorities. Time-consuming tuning and expertise are required to choose suitable task priorities, and to optimize their gains. Here, we automatically learn the controller configuration (soft and strict task priorities and Convergence Gains), looking for solutions that track a variety of desired task trajectories efficiently while preserving the robot's balance. We use multi-objective optimization to compare and choose among Pareto-optimal solutions that represent a trade-off of performance and robustness and can be transferred onto the real robot. We experimentally validate our method by learning a control configuration for the iCub humanoid, to perform different whole-body tasks, such as picking up objects, reaching and opening doors. |
| Databáze: | OpenAIRE |
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