Deka, Ankur and Luo, Wenhao and Li, Huao and Lewis, Michael
(2021)
Hiding Leader’s Identity in Leader-Follower Navigation
through Multi-Agent Reinforcement Learning.
In: Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
(In Press)
Abstract
Leader-follower navigation is a popular class of multi-robot algorithms where a leader robot leads the follower robots in a team. The leader has specialized capabilities or mission critical information (e.g. goal location) that the followers lack, and this makes the leader crucial for the mission’s success. However, this also makes the leader a vulnerability - an external adversary who wishes to sabotage the robot team’s mission can simply harm the leader and the whole robot team’s mission would be compromised. Since robot motion generated by traditional leader-follower navigation algorithms can reveal the identity of the leader, we propose a defense mechanism of hiding the leader’s identity by ensuring the leader moves in a way that behaviorally camouflages it with the followers, making it difficult for an adversary to identify the leader. To achieve this, we combine Multi-Agent Reinforcement Learning, Graph Neural Networks and adversarial training. Our approach enables the multi-robot team to optimize the primary task performance with leader motion similar to follower motion, behaviorally camouflaging it with the followers. Our algorithm outperforms existing work that tries to hide the leader’s identity in a multi-robot team by tuning traditional leader-follower control parameters with Classical Genetic Algorithms. We also evaluated human performance in inferring the leader’s identity and found that humans had lower accuracy when the robot team used our proposed navigation algorithm.
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Details
| Item Type: |
Conference or Workshop Item
(Paper)
|
| Status: |
In Press |
| Creators/Authors: |
|
| Date: |
2021 |
| Date Type: |
Publication |
| Journal or Publication Title: |
Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
| Publisher: |
IEEE |
| Event Title: |
Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
| Event Type: |
Conference |
| Schools and Programs: |
School of Computing and Information > Information Science |
| Refereed: |
Yes |
| Date Deposited: |
13 Aug 2021 20:05 |
| Last Modified: |
13 Aug 2021 20:05 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/41666 |
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