Skip to Main content Skip to Navigation
Conference papers

Staircase Traversal via Reinforcement Learning for Active Reconfiguration of Assistive Robots

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 metadatas

Cited literature [39 references]  Display  Hide  Download

https://hal-imt-atlantique.archives-ouvertes.fr/hal-02676585
Contributor : Sao Mai Nguyen <>
Submitted on : Sunday, May 31, 2020 - 6:31:11 PM
Last modification on : Tuesday, January 5, 2021 - 11:44:08 AM

Files

main.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02676585, version 1

Citation

Andrei Mitriakov, Panagiotis Papadakis, Sao Mai Nguyen, Serge Garlatti. Staircase Traversal via Reinforcement Learning for Active Reconfiguration of Assistive Robots. IEEE World Congress on Computational Intelligence, Jul 2020, Glasgow, United Kingdom. ⟨hal-02676585v1⟩

Share

Metrics

Record views

52

Files downloads

19