The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Dynamic Movement Primitives -A Framework for Motor Control in Humans and Humanoid Robotics
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Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
(2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 24,496
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
Advances in Neural Information Processing Systems 14: Proceedings of the 2001 Conference
(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 8,890
A robust layered control system for a mobile robot
Brooks, R. (1986) · IEEE Journal on Robotics and Automation · doi:10.1109/jra.1986.1087032 · Cited by 7,799 · cited 1×
“…or movement primitives, as for instance: u = π x, α ,t ( ) = π k x, α k ,t ( ) k=1 K ∑ (3) Indeed, related ideas have been suggested in various fields of research, for instance in computational neuroscience as Schema Theory [9] and in mobile robotics as behavior-based or reactive robotics [10].”
Background(2012) · Encyclopedia of the Sciences of Learning · doi:10.1007/978-1-4419-1428-6_5086 · Cited by 3,515
Dynamic patterns: The self-organization of brain and behavior
(1997) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(97)90151-6 · Cited by 2,839
Raibert, M. H., & Tello, E. R. (1986) · IEEE Expert · doi:10.1109/mex.1986.4307016 · Cited by 2,725
McKerrow, P. J. (1991) · Medical Entomology and Zoology · Cited by 1,731
(1980) · Advances in Psychology · doi:10.1016/s0166-4115(08)x6078-9 · Cited by 1,698
Cited by
The coordination of movement: optimal feedback control and beyond
Diedrichsen, J., Shadmehr, R., & Ivry, R. B. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2009.11.004 · Cited by 577
How to train your robot with deep reinforcement learning: Lessons we have learned
Ibarz, J., Tan, J., Finn, C., Kalakrishnan, M., Pastor, P., & Levine, S. (2021) · The International Journal of Robotics Research · doi:10.1177/0278364920987859 · Cited by 570
Dynamic movement primitives in robotics: A tutorial survey
Saveriano, M., Abu-Dakka, F. J., Kramberger, A., & Peternel, L. (2023) · The International Journal of Robotics Research · doi:10.1177/02783649231201196 · Cited by 267
Neuroengineering challenges of fusing robotics and neuroscience
Cheng, G., Ehrlich, S. K., Lebedev, M., & Nicolelis, M. A. L. (2020) · Science Robotics · doi:10.1126/scirobotics.abd1911 · Cited by 55
ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows
Urain, J., Ginesi, M., Tateo, D., & Peters, J. (2020) · 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341035 · Cited by 56
Metadata
- Authors
- Schaal, S.
- Venue
- Adaptive Motion of Animals and Machines
- Publisher
- Springer-Verlag
- Published
- 2006-07-27
- Pages
- 261-280
- OpenAlex
- W130216483
- Cited by
- 599
- References
- 60
- FWCI
- 7.22
- Type
- book chapter
- 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.
