@inproceedings{d201722a01d14e93bbffe754019f6e77,
title = "Learning-based Motion Stabilizer Leveraging Offline Temporal Optimization",
abstract = "During loco-manipulation, instabilities to the robot's base can be introduced by the manipulator's motions. Trajectories that are generated on-the-fly may jeopardize the stability and safety of the robot and its surroundings. This work proposes a self-supervised learning-based pipeline to keep a robot stable while executing a given trajectory. Empirical results show that the desired objective can be achieved with the proposed pipeline. Experiments are done in simulation and on hardware on a unique multi-modal, manipulation-capable legged robot, and its scalability is tested on a conventional manipulator.",
author = "Ahn, {Min Sung} and Hosik Chae and Colin Togashi and Dennis Hong and Joohyung Kim and Sungjoon Choi",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 19th International Conference on Ubiquitous Robots, UR 2022 ; Conference date: 04-07-2022 Through 06-07-2022",
year = "2022",
doi = "10.1109/UR55393.2022.9826279",
language = "English (US)",
series = "2022 19th International Conference on Ubiquitous Robots, UR 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "129--136",
booktitle = "2022 19th International Conference on Ubiquitous Robots, UR 2022",
address = "United States",
}