Meta Value Learning for Fast Policy-Centric Optimal Motion Planning

Siyuan Xu, Minghui Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper considers policy-centric optimal motion planning with limited reaction time. The motion planning queries are determined by their goal regions and cost functionals, and are generated over time from a distribution. Once a new query is requested, the robot needs to quickly generate a motion planner which can steer the robot to the goal region while minimizing a cost functional. We develop a meta-learning-based algorithm to compute a meta value function, which can be fast adapted using a small number of samples of a new query. Simulations on a unicycle are conducted to evaluate the developed algorithm and show the anytime property of the proposed algorithm.

Original languageEnglish (US)
Title of host publicationRobotics
Subtitle of host publicationScience and Systems
EditorsKris Hauser, Dylan Shell, Shoudong Huang
PublisherMIT Press Journals
ISBN (Print)9780992374785
StatePublished - 2022
Event18th Robotics: Science and Systems, RSS 2022 - New York City, United States
Duration: Jun 27 2022 → …

Publication series

NameRobotics: Science and Systems
ISSN (Electronic)2330-765X


Conference18th Robotics: Science and Systems, RSS 2022
Country/TerritoryUnited States
CityNew York City
Period6/27/22 → …

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this