Fast online segmentation of activities from partial trajectories
Name
ICRA19-Iqbal.pdf
Description
Accepted version
Size
1.91 MB
Format
Adobe PDF
Checksum (MD5)
2df9920c00354133ec7db2baa29bdcfc
Author(s)
Iqbal, Tariq
Li, Shen
Fourie, Christopher
Hayes, Bradley H
Shah, Julie A
Date Issued
2019
Journal
International Conference on Robotics and Automation (ICRA)
Publisher
IEEE
Citation
Iqbal, Tariq, et al., "Fast online segmentation of activities from partial trajectories." 2019 International Conference on Robotics and Automation (ICRA), May 20-24, 2019, Montreal, QC, IEEE, 2019: pp. 5019-25 doi 10.1109/ICRA.2019.8794054 ©2019 Author(s)
Version
Author's final manuscript
Abstract
Augmenting a robot with the capacity to understand the activities of the people it collaborates with in order to then label and segment those activities allows the robot to generate an efficient and safe plan for performing its own actions. In this work, we introduce an online activity segmentation algorithm that can detect activity segments by processing a partial trajectory. We model the transitions through activities as a hidden Markov model, which runs online by implementing an efficient particle-filtering approach to infer the maximum a posteriori estimate of the activity sequence. This process is complemented by an online search process to refine activity segments using task model information about the partial order of activities. We evaluated our algorithm by comparing its performance to two state-of-the-art activity segmentation algorithms on three human activity datasets. The proposed algorithm improved activity segmentation accuracy across all three datasets compared with the other two approaches, with a range from 11.3% to 65.5%, and could accurately recognize an activity through observation alone for 31.6% of the initial trajectory of that activity, on average. We also implemented the algorithm onto an industrial mobile robot during an automotive assembly task in which the robot tracked a human worker's progress and provided the worker with the correct materials at the appropriate time.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA.2019.8794054
