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Staircase Traversal via Reinforcement Learning for Active Reconfiguration of Assistive Robots

Andrei Mitriakov 1, 2 Panagiotis Papadakis 1, 2 Sao Mai Nguyen 2, 1 Serge Garlatti 2, 1
1 Lab-STICC_IMTA_CID_IHSEV
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : Assistive robots introduce a new paradigm for developing advanced personalized services. At the same time, the variability and stochasticity of environments, hardware and unknown parameters of the interaction complicates their modelling , as in the case of staircase traversal. For this task, we propose to treat the problem of robot configuration control within a reinforcement learning framework, using policy gradient optimization. In particular, we examine the use of safety or traction measures as a means for endowing the learned policy with desired properties. Using the proposed framework, we present extensive qualitative and quantitative results where a simulated robot learns to negotiate staircases of variable size, while being subjected to different levels of sensing noise.
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https://hal-imt-atlantique.archives-ouvertes.fr/hal-02676585
Contributor : Panagiotis Papadakis <>
Submitted on : Tuesday, December 15, 2020 - 11:59:03 AM
Last modification on : Tuesday, January 5, 2021 - 11:44:08 AM

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Andrei Mitriakov, Panagiotis Papadakis, Sao Mai Nguyen, Serge Garlatti. Staircase Traversal via Reinforcement Learning for Active Reconfiguration of Assistive Robots. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Jul 2020, Glasgow, United Kingdom. ⟨10.1109/FUZZ48607.2020.9177581⟩. ⟨hal-02676585v3⟩

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