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Learning-based modelling of physical interaction for assistive robots

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Abstract

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 and versions

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

Identifiers

  • HAL Id : hal-02341202 , version 1

Cite

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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