The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Self-Organization, Embodiment, and Biologically Inspired Robotics
Abstract
Robotics researchers increasingly agree that ideas from biology and self-organization can strongly benefit the design of autonomous robots. Biological organisms have evolved to perform and survive in a world characterized by rapid changes, high uncertainty, indefinite richness, and limited availability of information. Industrial robots, in contrast, operate in highly controlled environments with no or very little uncertainty. Although many challenges remain, concepts from biologically inspired (bio-inspired) robotics will eventually enable researchers to engineer machines for the real world that possess at least some of the desirable properties of biological organisms, such as adaptivity, robustness, versatility, and agility.
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Cites
The Embodied Mind: Cognitive Science and Human Experience
Varela, F. J., Rosch, E., & Thompson, E. (1991) · doi:10.7551/mitpress/6730.001.0001 · Cited by 5,765
The Embodied Mind: Cognitive Science and Human Experience
Varela, F. J., Thompson, E., & Rosch, E. (2017) · doi:10.7551/mitpress/9780262529365.001.0001 · Cited by 2,449
From Swimming to Walking with a Salamander Robot Driven by a Spinal Cord Model
Ijspeert, A. J., Crespi, A., Ryczko, D., & Cabelguen, J.-M. (2007) · Science · doi:10.1126/science.1138353 · Cited by 1,214
Socially intelligent robots: Dimensions of human-robot interaction
Dautenhahn, K. (2007) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2006.2004 · Cited by 1,210
The mechanics of active fin-shape control in ray-finned fishes
Alben, S., Madden, P. G., & Lauder, G. V. (2007) · Journal of The Royal Society Interface · doi:10.1098/rsif.2006.0181 · Cited by 160
Cited by
Soft robotics: Technologies and systems pushing the boundaries of robot abilities
Laschi, C., Mazzolai, B., & Cianchetti, M. (2016) · Science Robotics · doi:10.1126/scirobotics.aah3690 · Cited by 1,570
Blut, M., Wang, C., Wünderlich, N. V., & Brock, C. (2021) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-020-00762-y · Cited by 1,194
The grand challenges of Science Robotics
Yang, G.-Z., Bellingham, J., Dupont, P. E., Fischer, P., Floridi, L., Full, R., Jacobstein, N., Kumar, V., McNutt, M., Merrifield, R., et al. (2018) · Science Robotics · doi:10.1126/scirobotics.aar7650 · Cited by 1,177
A 3D-printed, functionally graded soft robot powered by combustion
Bartlett, N. W., Tolley, M. T., Overvelde, J. T. B., Weaver, J. C., Mosadegh, B., Bertoldi, K., Whitesides, G. M., & Wood, R. J. (2015) · Science · doi:10.1126/science.aab0129 · Cited by 1,052
Bio-inspired magnetic swimming microrobots for biomedical applications
Peyer, K. E., Zhang, L., & Nelson, B. J. (2013) · Nanoscale · doi:10.1039/c2nr32554c · Cited by 820
Metadata
- Authors
- Pfeifer, R., Lungarella, M., & Iida, F.
- Venue
- Science
- Published
- 2007-11-16
- Volume
- 318
- Issue
- 5853
- Pages
- 1088-1093
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2090252028
- Cited by
- 1,227
- References
- 5
- FWCI
- 25.37
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
