The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Validating Human–Robot Interaction Schemes in Multitasking Environments
Abstract
The ability of robots to autonomously perform tasks is increasing. More autonomy in robots means that the human managing the robot may have available free time. It is desirable to use this free time productively, and a current trend is to use this available free time to manage multiple robots. We present the notion of neglect tolerance as a means for determining how robot autonomy and interface design determine how free time can be used to support multitasking, in general, and multirobot teams, in particular. We use neglect tolerance to 1) identify the maximum number of robots that can be managed; 2) identify feasible configurations of multirobot teams; and 3) predict performance of multirobot teams under certain independence assumptions. We present a measurement methodology, based on a secondary task paradigm, for obtaining neglect tolerance values that allow a human to balance workload with robot performance.
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Citations
Cites
Changes in executive control across the life span: Examination of task-switching performance.
Cepeda, N. J., Kramer, A. F., & Gonzalez de Sather, J. C. M. (2001) · Developmental Psychology · doi:10.1037/0012-1649.37.5.715 · Cited by 657
Cited by
Human–Robot Interaction: A Survey
Goodrich, M. A., & Schultz, A. C. (2008) · Foundations and Trends® in Human–Computer Interaction · doi:10.1561/1100000005 · Cited by 1,541
Self-citationTwo invariants of human-swarm interaction
Brown, D. S., Goodrich, M. A., Jung, S.-Y., & Kerman, S. C. (2015) · Journal of Human-Robot Interaction · doi:10.5898/jhri.5.1.brown · Cited by 38
Self-citationLazyDAgger: Reducing Context Switching in Interactive Imitation Learning
Hoque, R., Balakrishna, A., Putterman, C., Luo, M., Brown, D. S., Seita, D., Thananjeyan, B., Novoseller, E., & Goldberg, K. (2021) · 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case49439.2021.9551469 · Cited by 31
“However, the above algorithms assume that the human is continuously monitoring the system to determine when to intervene, which may not be practical in large-scale systems or continuous learning settings [12, 14, 25, 46].”
BackgroundThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning
Hoque, R., Balakrishna, A., Novoseller, E., Wilcox, A., Brown, D. S., & Goldberg, K. (2021) · arXiv · doi:10.48550/arxiv.2109.08273 · Cited by 31
Metadata
- Authors
- Crandall, J. W., Goodrich, M. A., Olsen, D. R., & Nielsen, C. W.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
- Published
- 2005-07
- Volume
- 35
- Issue
- 4
- Pages
- 438-449
- ISSN
- 1083-4427 (ISSN-L)
- OpenAlex
- W2311543941
- Cited by
- 268
- References
- 1
- FWCI
- 17.64
- Type
- journal article
- Language
- en
- Open access
- closed
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