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 [BibTeX] [Marc21]
Learning Joint Space Reference Manifold for Reliable Physical Assistance
Type of publication: Conference paper
Citation: Razmjoo_IROS_2023
Publication status: Published
Booktitle: Proc. IEEE/RSJ Intl Conf. on Intelligent Robots and Systems (IROS)
Year: 2023
Pages: 10412-10417
ISSN: 2153-0858
ISBN: 978-1-6654-9190-7
DOI: https://doi.org/10.1109/IROS55552.2023.10342173
Abstract: This paper presents a study on the use of the Talos humanoid robot for performing assistive sit-to-stand or stand-to-sit tasks. In such tasks, the human exerts a large amount of force (100-200 N) within a very short time (2-8 s), posing significant challenges in terms of human unpredictability and robot stability control. To address these challenges, we propose an approach for finding a spatial reference for the robot, which allows the robot to move according to the force exerted by the human and control its stability during the task. Specifically, we focus on the problem of finding a 1D manifold for the robot, while assuming a simple controller to guide its movement on this manifold. To achieve this, we use a functional representation to parameterize the manifold and solve an optimization problem that takes into account the robot's stability and the unpredictability of human behavior. We demonstrate the effectiveness of our approach through simulations and experiments with the Talos robot, showing robustness and adaptability.
Projects Idiap
Authors Razmjoo, Amirreza
Brecelj, Tilen
Savevska, Kristina
Ude, Ales
Petric, Tadej
Calinon, Sylvain
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  • Razmjoo_IROS_2023.pdf