Human-robot collaboration in manufacturing: Quantitative evaluation of predictable, convergent joint action
Name
Shah_Human-robot.pdf
Size
454.51 KB
Format
Adobe PDF
Checksum (MD5)
404a9e2560845f0d0c4fdeb740e556d2
Author(s)
Nikolaidis, Stefanos
Rossano, Gregory
Martinez, Carlos
Fuhlbrigge, Thomas
Lasota, Przemyslaw Andrzej
Shah, Julie A.
Date Issued
October 2013
Journal
Proceedings of the 2013 IEEE 44th International Symposium on Robotics
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Nikolaidis, Stefanos, Przemyslaw Lasota, Gregory Rossano, Carlos Martinez, Thomas Fuhlbrigge, and Julie Shah. “Human-Robot Collaboration in Manufacturing: Quantitative Evaluation of Predictable, Convergent Joint Action.” IEEE ISR 2013 (October 2013).
Version
Author's final manuscript
Abstract
New industrial robotic systems that operate in the same physical space as people highlight the emerging need for robots that can integrate seamlessly into human group dynamics. In this paper we build on our prior investigation, which evaluates the convergence of a robot computational teaming model and a human teammate's mental model, by computing the entropy rate of the Markov chain. We present and analyze the six out of thirty-six human trials where the human participant switched execution strategies while working with the robot. We conduct a post-hoc analysis of this dataset and show that the entropy rate appears to be sensitive to changes in the human strategy and reflects the resulting increase in uncertainty about the human next actions. We propose that these results provide first support that entropy rate may be used as a component of dynamic risk assessment, to generate risk-aware robot motions and action selections.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ISR.2013.6695625
