The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Biped dynamic walking using reinforcement learning
Overview
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Citations
Cites
Learning to predict by the methods of temporal differences
Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148
WIDROW, B., & HOFF, M. E. (1960) · doi:10.21236/ad0241531 · Cited by 4,009
A New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)
Albus, J. S. (1975) · Journal of Dynamic Systems, Measurement, and Control · doi:10.1115/1.3426922 · Cited by 2,177 · cited 1×
“3.3 CMAC The Cerebellar Model Arithmetic Computer (CMAC) [Albus, 75] is an associative neural network that tries to mimic the biological sensory neurons in the brain’s cerebellum.”
BackgroundInfluentialForward models: supervised learning with a distal teacher
Jordan, M. I., & Rumelhart, D. E. (1992) · Cognitive Science · doi:10.1207/s15516709cog1603_1 · Cited by 1,451
Cited by
Reinforcement learning in robotics: A survey
Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160
Efficient Bipedal Robots Based on Passive-Dynamic Walkers
Collins, S., Ruina, A., Tedrake, R., & Wisse, M. (2005) · Science · doi:10.1126/science.1107799 · Cited by 1,873
Nguyen, T. T., Nguyen, N. D., & Nahavandi, S. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2977374 · Cited by 1,306
A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients
Grondman, I., Busoniu, L., Lopes, G. A. D., & Babuska, R. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2012.2218595 · Cited by 1,042
Reinforcement learning of motor skills with policy gradients
Peters, J., & Schaal, S. (2008) · Neural Networks · doi:10.1016/j.neunet.2008.02.003 · Cited by 831
Metadata
- Authors
- Benbrahim, H., & Franklin, J. A.
- Venue
- Robotics and Autonomous Systems
- Publisher
- Elsevier BV
- Published
- 1997-12
- Volume
- 22
- Issue
- 3-4
- Pages
- 283-302
- ISSN
- 0921-8890 (ISSN-L)
- OpenAlex
- W1994923984
- Cited by
- 198
- References
- 4
- FWCI
- 0.77
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Biped dynamic walking using reinforcement learning. https://app.cochat.ai/papers/10.1016/s0921-8890(97)00043-2
- The paper
- Benbrahim, H., & Franklin, J. A. (1997). Biped dynamic walking using reinforcement learning. Robotics and Autonomous Systems. https://doi.org/10.1016/s0921-8890(97)00043-2
Data sources
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.
