Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Multimodal anomaly detection for assistive robots

Multimodal anomaly detection for assistive robots Detecting when something unusual has happened could help assistive robots operate more safely and effectively around people. However, the variability associated with people and objects in human environments can make anomaly detection difficult. We previously introduced an algorithm that uses a hidden Markov model (HMM) with a log-likelihood detection threshold that varies based on execution progress. We now present an improved version of our previous algorithm (HMM-D) and introduce a new algorithm based on Gaussian process regression (HMM-GP). We also present a new and more thorough evaluation of 8 anomaly detection algorithms with force, sound, and kinematic signals collected from a robot closing microwave doors, latching a toolbox, scooping yogurt, and feeding yogurt to able-bodied participants. Overall, HMM-GP had the highest performance in terms of area under the curve for these real-world tasks, and multiple modalities improved performance with some anomalies being better detected with particular modalities. With synthetic anomalies, HMM-D exhibited shorter detection delays and outperformed HMM-GP with high-magnitude anomalies. In general, higher-magnitude synthetic anomalies tended to be detected more rapidly. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Autonomous Robots Springer Journals

Multimodal anomaly detection for assistive robots

Park, Daehyung; Kim, Hokeun; Kemp, Charles
Autonomous Robots , Volume 43 (3) – Apr 13, 2018
Download PDF
19 pages

Loading next page...
 
/lp/springer-journals/multimodal-anomaly-detection-for-assistive-robots-MdAyLimLcQ

References (84)

Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Engineering; Robotics and Automation; Artificial Intelligence; Computer Imaging, Vision, Pattern Recognition and Graphics; Control, Robotics, Mechatronics
ISSN
0929-5593
eISSN
1573-7527
DOI
10.1007/s10514-018-9733-6
Publisher site
See Article on Publisher Site

Abstract

Detecting when something unusual has happened could help assistive robots operate more safely and effectively around people. However, the variability associated with people and objects in human environments can make anomaly detection difficult. We previously introduced an algorithm that uses a hidden Markov model (HMM) with a log-likelihood detection threshold that varies based on execution progress. We now present an improved version of our previous algorithm (HMM-D) and introduce a new algorithm based on Gaussian process regression (HMM-GP). We also present a new and more thorough evaluation of 8 anomaly detection algorithms with force, sound, and kinematic signals collected from a robot closing microwave doors, latching a toolbox, scooping yogurt, and feeding yogurt to able-bodied participants. Overall, HMM-GP had the highest performance in terms of area under the curve for these real-world tasks, and multiple modalities improved performance with some anomalies being better detected with particular modalities. With synthetic anomalies, HMM-D exhibited shorter detection delays and outperformed HMM-GP with high-magnitude anomalies. In general, higher-magnitude synthetic anomalies tended to be detected more rapidly.

Journal

Autonomous Robots – Springer Journals

Published: Apr 13, 2018

There are no references for this article.