@inproceedings{8abf5cfbd99a4c399877bea61852e7ad,
title = "Adaptive Slope Locomotion with Deep Reinforcement Learning",
abstract = "In this paper we present a model free Deep Reinforcement Learning based approach to the motion planning problem of a quadruped moving from a flat to an inclined plane. In our implementation, we do not provide any prior information of the location of the inclined plane, nor pass any vision data during the training process. With this approach, we train a 12 degree of freedom quadruped robot to traverse up and down a variety of simulated sloped environments, in the process demonstrating that deep reinforcement learning is able to generate highly dynamic and adaptable solutions.",
keywords = "DPPO, Quadruped, Reinforcement Learning, Slope Walking, V-REP",
author = "William Jones and Tamir Blum and Kazuya Yoshida",
note = "Funding Information: *This work was partly supported by JSPS KAKENHI Grant Number 19J20685. Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE/SICE International Symposium on System Integration, SII 2020 ; Conference date: 12-01-2020 Through 15-01-2020",
year = "2020",
month = jan,
doi = "10.1109/SII46433.2020.9025928",
language = "English",
series = "Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "546--550",
booktitle = "Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020",
address = "United States",
}