Stackelberg Meta-Learning for Strategic Guidance in Multi-Robot Trajectory Planning

Yuhan Zhao, Quanyan Zhu

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

Abstract

Trajectory guidance requires a leader robotic agent to assist a follower robotic agent to cooperatively reach the target destination. However, planning cooperation becomes difficult when the leader serves a family of different followers and has incomplete information about the followers. There is a need for learning and fast adaptation of different cooperation plans. We develop a Stackelberg meta-learning approach to address this challenge. We first formulate the guided trajectory planning problem as a dynamic Stackelberg game to capture the leader-follower interactions. Then, we leverage meta-learning to develop cooperative strategies for different followers. The leader learns a meta-best-response model from a prescribed set of followers. When a specific follower initiates a guidance query, the leader quickly adapts to the follower-specific model with a small amount of learning data and uses it to perform trajectory guidance. We use simulations to elaborate that our method provides a better generalization and adaptation per-formance on learning followers' behavior than other learning approaches. The value and the effectiveness of guidance are also demonstrated by the comparison with zero guidance scenarios11The simulation codes are available at https://github.com/yuhan16/Stackelberg-Meta-Learning..

Original languageEnglish (US)
Title of host publication2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages11342-11347
Number of pages6
ISBN (Electronic)9781665491907
DOIs
StatePublished - 2023
Event2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023 - Detroit, United States
Duration: Oct 1 2023Oct 5 2023

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
Country/TerritoryUnited States
CityDetroit
Period10/1/2310/5/23

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Stackelberg Meta-Learning for Strategic Guidance in Multi-Robot Trajectory Planning'. Together they form a unique fingerprint.

Cite this