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Poster De Conférence Année : 2019

Learning-based modelling of physical interaction for assistive robots

Résumé

Deploying companion robots for assisting humans requires safe and robust interaction with the environment, both in terms of mobility and object manipulation. To extend a robot's workspace, we are here concerned with multifloor operation and staircase traversal, as a building block for the development of object fetching services. In this article, we advocate a lifelong learning treatment of this problem within a reinforcement learning (RL) framework. In view of sparse earlier work for the scenario of interest, we hereby identify relevant methodological aspects and report our preliminary developments.
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Dates et versions

hal-02341202 , version 1 (31-10-2019)

Identifiants

  • HAL Id : hal-02341202 , version 1

Citer

Andrei Mitriakov, Panagiotis Papadakis, Sao Mai Nguyen, Serge Garlatti. Learning-based modelling of physical interaction for assistive robots. Journées Francophones sur la Planification, la Décision et l'Apprentissage pour la conduite de systèmes (JFPDA), Jul 2019, Toulouse, France. ⟨hal-02341202⟩
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