Staircase Traversal via Reinforcement Learning for Active Reconfiguration of Assistive Robots - Archive ouverte HAL Access content directly
Conference Papers Year : 2020

Staircase Traversal via Reinforcement Learning for Active Reconfiguration of Assistive Robots

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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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Dates and versions

hal-02676585 , version 1 (31-05-2020)
hal-02676585 , version 2 (10-06-2020)
hal-02676585 , version 3 (15-12-2020)

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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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