IJCAI 2021poster1 citations
Planning and Reinforcement Learning for General-Purpose Service Robots
Abstract
Despite recent progress in AI and robotics research, especially learned robot skills, there remain significant challenges in building robust, scalable, and general-purpose systems for service robots. This Ph.D. research aims to combine symbolic planning and reinforcement learning to reason about high-level robot tasks and adapt to the real world. We will introduce task planning algorithms that adapt to the environment and other agents, as well as reinforcement learning methods that are practical for service robot systems. Taken together, this work will make a significant step towards creating general-purpose service robots.
Robotics: Cognitive RoboticsPlanning and Scheduling: Robot PlanningMachine Learning: Reinforcement LearningRobotics: Learning in Robotics
BibTeX
@inproceedings{ijcai2021p679,
title = {Planning and Reinforcement Learning for General-Purpose Service Robots},
author = {Jiang, Yuqian},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Zhi-Hua Zhou},
pages = {4895--4896},
year = {2021},
month = {8},
note = {Doctoral Consortium},
doi = {10.24963/ijcai.2021/679},
url = {https://doi.org/10.24963/ijcai.2021/679},
}