research-article

Unsupervised Learning of Qualitative Motion Behaviours by a Mobile Robot

Authors:

Paul Duckworth,

Yiannis Gatsoulis,

Anthony G. CohnAuthors Info & Claims

AAMAS '16: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems

Pages 1043 - 1051

Published: 09 May 2016 Publication History

Abstract

The success of mobile robots, in daily living environments, depends on their capabilities to understand human movements and interact in a safe manner. This paper presents a novel unsupervised qualitative-relational framework for learning human motion patterns using a single mobile robot platform. It is capable of learning human motion patterns in real-world environments, in order to predict future behaviours.

This previously untackled task is challenging because of the limited field of view provided by a single mobile robot. It is only able to observe one location at any time, resulting in incomplete and partial human detections and trajectories. Central to the success of the presented framework is mapping the detections into an abstract qualitative space, and then characterising motion invariant to exact metric position.

This framework was used by a physical robot autonomously patrolling a company's office during a six week deployment. Experimental results from this deployment are discussed and demonstrate the effectiveness and applicability of the system.

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Cited By

Duckworth PAlomari MGatsoulis YHogg DCohn A(2016)Unsupervised activity recognition using latent semantic analysis on a mobile robotProceedings of the Twenty-second European Conference on Artificial Intelligence10.3233/978-1-61499-672-9-1062(1062-1070)Online publication date: 29-Aug-2016
https://dl.acm.org/doi/10.3233/978-1-61499-672-9-1062

Index Terms

Unsupervised Learning of Qualitative Motion Behaviours by a Mobile Robot
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Activity recognition and understanding
    2. Knowledge representation and reasoning
      1. Spatial and physical reasoning
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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Information & Contributors

Information

Published In

cover image ACM Other conferences

AAMAS '16: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems

May 2016

1580 pages

ISBN:9781450342391

General Chairs:
Catholijn M. Jonker
TU Delft, Netherlands
,
Stacy Marsella
University of Southern California, USA
,
Program Chairs:
John Thangarajah
RMIT University. Australia
,
Karl Tuyls
University of Liverpool, UK

Sponsors

IFAAMAS

In-Cooperation

SIGAI: ACM Special Interest Group on Artificial Intelligence

Publisher

International Foundation for Autonomous Agents and Multiagent Systems

Richland, SC

Publication History

Published: 09 May 2016

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EU FP7 project 600623 (STRANDS)

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AAMAS '16

Sponsor:

AAMAS '16: International Conference on Agents and Multiagent Systems

May 9 - 13, 2016

Singapore, Singapore

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AAMAS '16 Paper Acceptance Rate 137 of 550 submissions, 25%;

Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

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Cited By

Duckworth PAlomari MGatsoulis YHogg DCohn A(2016)Unsupervised activity recognition using latent semantic analysis on a mobile robotProceedings of the Twenty-second European Conference on Artificial Intelligence10.3233/978-1-61499-672-9-1062(1062-1070)Online publication date: 29-Aug-2016
https://dl.acm.org/doi/10.3233/978-1-61499-672-9-1062

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