Intelligent Heterogeneous Multi-robot Catching System using Deep Multi-agent Reinforcement Learning 

Authors: Yuan Gao, Junfeng Chen, Xi Chen, Chongyang Wang, Junjie Hu, Fuqin Deng, Tin Lun Lam
Corresponding author: Tin Lun Lam (Email: tllam@cuhk.edu.cn; Website: sites.google.com/site/lamtinlun ​)
Published in: IEEE Transactions on Robotics (T-RO)
Paper: freeformrobotics.org/wp-conte...
Freeform Robotics: freeformrobotics.org
Title:
Asymmetric Self-play Enabled Intelligent Heterogeneous Multi-robot Catching System using Deep Multi-agent Reinforcement Learning
Abstract:
Aiming to develop a more robust and intelligent heterogeneous system for adversarial catching in security & rescue tasks, in this paper, 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 multi-robot systems (HMRS). Our method, based on actor-critic multi-agent reinforcement learning, provides a framework that can enable cooperative behaviours among heterogeneous multi-robot 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.

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