---
title: "Planning and Reinforcement Learning for General-Purpose Service Robots"
authors:
  - "Yuqian Jiang"
doi: "10.24963/ijcai.2021/679"
url: "https://ijcai.org/proceedings/2021/679"
pdf: "https://ijcai.org/proceedings/2021/0679.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "4895-4896"
track: "Doctoral Consortium"
keywords:
  - "Robotics: Learning in Robotics"
  - "Planning and Scheduling: Robot Planning"
  - "Robotics: Cognitive Robotics"
  - "Machine Learning: Reinforcement Learning"
---

# Planning and Reinforcement Learning for General-Purpose Service Robots

Yuqian Jiang

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 4895-4896. DOI: [10.24963/ijcai.2021/679](https://doi.org/10.24963/ijcai.2021/679)

## 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.