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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, 3, 4 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
4 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
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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Submitted on : Wednesday, June 10, 2020 - 5:29:56 PM
Last modification on : Wednesday, October 14, 2020 - 4:12:14 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. 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Jul 2020, Glasgow, United Kingdom. ⟨10.1109/FUZZ48607.2020.9177581⟩. ⟨hal-02676585v2⟩

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