The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Learning motor primitives for robotics
Abstract
The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Motor primitives offer one of the most promising frameworks for the application of machine learning techniques in this context. Employing an improved form of the dynamic systems motor primitives originally introduced by Ijspeert et al. [2], we show how both discrete and rhythmic tasks can be learned using a concerted approach of both imitation and reinforcement learning. For doing so, we present both learning algorithms and representations targeted for the practical application in robotics. Furthermore, we show that it is possible to include a start-up phase in rhythmic primitives. We show that two new motor skills, i.e., Ball-in-a-Cup and Ball-Paddling, can be learned on a real Barrett WAM robot arm at a pace similar to human learning while achieving a significantly more reliable final performance.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
An introduction to MCMC for machine learning.
Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003) · Machine Learning · doi:10.1023/a:1020281327116 · Cited by 2,451
Motor primitives in vertebrates and invertebrates
Flash, T., & Hochner, B. (2005) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2005.10.011 · Cited by 508 · cited 1×
“, paddling a ball with a table-tennis racket or hitting a tennis ball, it is highly likely that they rely on a small set of motor primitives and use imitation as well as reinforcement learning [1].”
BackgroundA Kendama Learning Robot Based on Bi-directional Theory
Miyamoto, H., Schaal, S., Gandolfo, F., Gomi, H., Koike, Y., Osu, R., Nakano, E., Wada, Y., & Kawato, M. (1996) · Neural Networks · doi:10.1016/s0893-6080(96)00043-3 · Cited by 171
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
Self-citationImitation Learning: A Survey of Learning Methods
Hussein, A., Gaber, M. M., Elyan, E., & Jayne, C. (2018) · ACM Computing Surveys · doi:10.1145/3054912 · Cited by 1,060
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
Rajeswaran, A., Kumar, V., Gupta, A., Vezzani, G., Schulman, J., Todorov, E., & Levine, S. (2018) · Robotics: Science and Systems XIV · doi:10.15607/rss.2018.xiv.049 · Cited by 847
A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots
Giusti, A., Guzzi, J., Ciresan, D. C., He, F.-L., Rodriguez, J. P., Fontana, F., Faessler, M., Forster, C., Schmidhuber, J., Caro, G. D., et al. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2015.2509024 · Cited by 665
An algorithmic perspective on imitation learning
Osa, T., Pajarinen, J., Neumann, G., Bagnell, J. A., Abbeel, P., & Peters, J. (2018) · Foundations and Trends in Robotics · doi:10.1561/2300000053 · Cited by 405
Self-citation
Metadata
- Authors
- Kober, J., & Peters, J.
- Venue
- 2009 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2009-05
- Pages
- 2112-2118
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2147162091
- Cited by
- 220
- References
- 4
- FWCI
- 11.52
- Type
- conference paper
- 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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Learning motor primitives for robotics. https://app.cochat.ai/papers/10.1109/robot.2009.5152577
- The paper
- Kober, J., & Peters, J. (2009). Learning motor primitives for robotics. 2009 IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/robot.2009.5152577
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.
