Automatic Discovery of Motion Patterns that Improve Learning Rate in Communication-Limited Multi-Robot Systems

Taeyeong Choi, Theodore P. Pavlic

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

Abstract

Learning in robotic systems is largely constrained by the quality of the training data available to a robot learner. Robots may have to make multiple, repeated expensive excursions to gather this data or have humans in the loop to perform demonstrations to ensure reliable performance. The cost can be much higher when a robot embedded within a multi-robot system must learn from the complex aggregate of the many robots that surround it and may react to the learner's motions. In our previous work [1], [2], we considered the problem of Remote Teammate Localization (ReTLo), where a single robot in a team uses passive observations of a nearby neighbor to accurately infer the position of robots outside of its sensory range even when robot-to-robot communication is not allowed in the system. We demonstrated a communication-free approach to show that the rearmost robot can use motion information of a single robot within its sensory range to predict the positions of all robots in the convoy. Here, we expand on that work with Selective Random Sampling (SRS), a framework that improves the ReTLo learning process by enabling the learner to actively deviate from its trajectory in ways that are likely to lead to better training samples and consequently gain accurate localization ability with fewer observations. By adding diversity to the learner's motion, SRS simultaneously improves the learner's predictions of all other teammates and thus can achieve similar performance as prior methods with less data.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages243-248
Number of pages6
ISBN (Electronic)9781728164229
DOIs
StatePublished - Sep 14 2020
Event2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2020 - Karlsruhe, Germany
Duration: Sep 14 2020Sep 16 2020

Publication series

NameIEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
Volume2020-September

Conference

Conference2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2020
CountryGermany
CityKarlsruhe
Period9/14/209/16/20

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Science Applications

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