Asymmetric Self-Play-Enabled Intelligent Heterogeneous Multirobot Catching System Using Deep Multiagent Reinforcement Learning

Aiming to develop a more robust and intelligent heterogeneous system for adversarial catching in security and rescue tasks, in this article, we discuss the specialities of applying asymmetric self-play and curriculum learning techniques to deal with the increasing heterogeneity and number of differe...

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Bibliographic Details
Published in:IEEE transactions on robotics 2023-08, Vol.39 (4), p.2603-2622
Main Authors: Gao, Yuan, Chen, Junfeng, Chen, Xi, Wang, Chongyang, Hu, Junjie, Deng, Fuqin, Lam, Tin Lun
Format: Article
Language:English
Subjects:
Asymmetric self-play
Asymmetry
catching systems
Constraint modelling
Curricula
Heterogeneity
heterogeneous multirobot system (HMRS)
Heuristic algorithms
Mathematical models
Multi-robot systems
Multiagent systems
Multiple robots
Planning
reinforcement learning (RL)
Robots
Security
Task analysis
Teams
Training
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Summary:Aiming to develop a more robust and intelligent heterogeneous system for adversarial catching in security and rescue tasks, in this article, we discuss the specialities of applying asymmetric self-play and curriculum learning techniques to deal with the increasing heterogeneity and number of different robots in modern heterogeneous multirobot systems (HMRS). Our method, based on actor-critic multiagent reinforcement learning, provides a framework that can enable cooperative behaviors among heterogeneous multirobot teams. This leads to the development of an HMRS for complex catching scenarios that involve several robot teams and real-world constraints. We conduct simulated experiments to evaluate different mechanisms' influence on our method's performance, and real-world experiments to assess our system's performance in complex real-world catching problems. In addition, a bridging study is conducted to compare our method with a state-of-the-art method called S2M2 in heterogeneous catching problems, and our method performs better in adversarial settings. As a result, we show that the proposed framework, through fusing asymmetric self-play and curriculum learning during training, is able to successfully complete the HMRS catching task under realistic constraints in both simulation and the real world, thus providing a direction for future large-scale intelligent security & rescue HMRS.
ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2023.3257541