Skip to Main content Skip to Navigation
New interface
Conference papers

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 
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.
Complete list of metadata

Cited literature [28 references]  Display  Hide  Download
Contributor : Sao Mai Nguyen Connect in order to contact the contributor
Submitted on : Wednesday, June 10, 2020 - 5:29:56 PM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM


Files produced by the author(s)




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⟩



Record views


Files downloads