Learning Multi-Granularity Task Primitives from Construction Videos for Human-Robot Collaboration-Reference-Cited by-同舟云学术

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Learning Multi-Granularity Task Primitives from Construction Videos for Human-Robot Collaboration

Published:2024-01-25 Issue: Volume:5 Page:674-681
ISSN:
Container-title:Computing in Civil Engineering 2023
language:
Short-container-title:

Author:

Pan Zaolin¹²,Yu Yantao¹²

Affiliation:

1. Ph.D. Student, Dept. of Civil and Environmental Engineering, Hong Kong Univ. of Science and Technology, Hong Kong, China.

2. Assistant Professor, Dept. of Civil and Environmental Engineering, Hong Kong Univ. of Science and Technology, Hong Kong, China.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485224.081

Reference16 articles.

1. Bock, T., and T. Linner, eds. 2016. “Single-Task Construction Robots by Category.” Constr. Robot., 14–290. Cambridge: Cambridge University Press.

2. Towards Efficient Human-Robot Collaboration With Robust Plan Recognition and Trajectory Prediction;Cheng Y.;IEEE Robot. Autom. Lett,2020

3. Computer Vision-Based Video Interpretation Model for Automated Productivity Analysis of Construction Operations

4. Learning and classifying actions of construction workers and equipment using Bag-of-Video-Feature-Words and Bayesian network models

5. Grigore E. C. A. Roncone O. Mangin and B. Scassellati. 2018. “Preference-Based Assistance Prediction for Human-Robot Collaboration Tasks.” 2018 IEEE/RSJ Int. Conf. Intell. Robot. Syst. 4441–4448.

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