The DOI matches its Crossref record; 113 of the 117 references it cites were found in a registry or an index.
A tutorial on task-parameterized movement learning and retrieval
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Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The authors show how different trajectories can be generated from the same set of task parameters through weighting of the learned local models.”
From Improving Task-Parameterised Movement Learning Generalisation with Frame-Weighted Trajectory Generation (2019) · cites this paper as [7]
In the citing paper
“To address these issues, this paper builds on the confidence weighting scheme presented in [7] and the frame importance sampling in [8], and presents an autonomous method for weighting task frames during regression to improve model extrapolation, while retaining local structure observed in demonstration data.”
From Improving Task-Parameterised Movement Learning Generalisation with Frame-Weighted Trajectory Generation (2019) · cites this paper as [7]
In the citing paper
“The authors propose this is a useful mechanism for providing human prior knowledge about the importance of different task parameters to the model.”
From Improving Task-Parameterised Movement Learning Generalisation with Frame-Weighted Trajectory Generation (2019) · cites this paper as [7]
Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
Tensor Decompositions and Applications
Kolda, T. G., & Bader, B. W. (2009) · SIAM Review · doi:10.1137/07070111x · Cited by 10,624
On Spectral Clustering: Analysis and an algorithm
Ng, A. Y., Jordan, M. I., & Weiss, Y. (2001) · Cited by 8,159
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,330
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,050
Optimal feedback control as a theory of motor coordination
Todorov, E., & Jordan, M. I. (2002) · Nature Neuroscience · doi:10.1038/nn963 · Cited by 3,081
Apprenticeship learning via inverse reinforcement learning
Abbeel, P., & Ng, A. Y. (2004) · International Conference on Machine Learning · doi:10.1145/1015330.1015430 · Cited by 2,855
Research works printed but not linked: 3
- Krishnan S, Garg A, Patil S, Lea C, Hager G, Abbeel P, Goldberg K (2015) Unsupervised surgical task segmentation with milestone learning. In: Proceedings of international symposium on robotics research (ISRR)
- MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of of the 5th Berkeley symposium on mathematical statistics and probability, pp 281–297
- Wang Y, Zhu J (2015) DP-space: Bayesian nonparametric subspace clustering with small-variance asymptotics. In: Proceedings of international conference on machine learning (ICML), pp 1–9. Lille, France
Cited by
Sorted by citations
Cobot programming for collaborative industrial tasks: An overview
Zaatari, S. E., Marei, M., Li, W., & Usman, Z. (2019) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2019.03.003 · Cited by 470
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
“The approach based on TP-GMM has been recently employed in several studies [Calinon, 2016, Rozo et al., 2016].”
MethodAn Algorithmic Perspective on Imitation Learning
Osa, T., Pajarinen, J., Neumann, G. M., Bagnell, J. A., Abbeel, P., & Peters, J. (2018) · now publishers, Inc. eBooks · doi:10.1561/9781680834116 · Cited by 394
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
“The authors (Chen et al. 2015, 2016) introduced a framework called AutoEncoded DMP (AEDMP) which uses deep auto-encoders to find a movements represented in latent feature space.”
BackgroundKernelized movement primitives
Huang, Y., Rozo, L. D., Silvério, J., & Caldwell, D. G. (2019) · The International Journal of Robotics Research · doi:10.1177/0278364919846363 · Cited by 263
“In order to address this problem, we extend KMP by using local coordinate systems and affine transformations as in Calinon (2016); Huang et al. (2018), which allows KMP to exhibit better extrapolation capabilities (Section 3.3).”
Influential
Metadata
- Authors
- Calinon, S.
- Affiliations
- Idiap Research Institute (CH)
- Corresponding author
- Calinon, Sylvain
- ORCID
- Calinon, Sylvain
- Venue
- Intelligent Service Robotics
- Published
- 2015-09-26
- Volume
- 9
- Issue
- 1
- Pages
- 1-29
- ISSN
- 1861-2776 (ISSN-L)
- 1861-2784
- OpenAlex
- W2100993276
- Cited by
- 422
- References
- 117
- Citations by year
- 2026: 31 · 2025: 48 · 2024: 43 · 2023: 40 · 2022: 30 · 2021: 63 · 2020: 41 · 2019: 49 · 2018: 43 · 2017: 26 · 2016: 8
- FWCI
- 23.76
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Intelligent Service Robotics (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
- Funders
- EC Horizon 2020 (635491)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Human Motion and Animation
- Keywords
- reference frames, coordinate systems, basis functions, service robotics, robotic manipulation, behavior transfer
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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.
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