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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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
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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
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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
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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
Flagged first, then by citations
4 of the 117 references couldn’t be matched to a known paper.
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
Items 401–427 of 427
Individualized Assistive Torque Planning Strategy Coupled with CoM for Lower Limb Exoskeletons
Song, G., Ou, Y., Huang, B., Zou, C., Peng, Z., Huang, L., Chen, L., Li, W., Cheng, H., & Qiu, J. (2025) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio66223.2025.11376154
“These probabilistic models are then processed via Gaussian Mixture Regression (GMR)[22] to derive the reference torque trajectory in this context, ˆ μ i and ˆΣ i represent mean and covariance of the conditional probability distribution P (ˆ τ | t i ) , respectively.”
GeoSACS: Geometric Shared Autonomy via Canal Surfaces
Rajapakshe, S., Dastenavar, A., Hagenow, M., Odobez, J., & Senft, E. (2026) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3757279.3785579
“…include movement primitives (e.g., dynamic movement primitives (DMPs) [21], probabilistic movement primitives [33]), Gaussian mixture regression [8, 9], dynamical systems (e.g., SEDs [23]), and inverse reinforcement learning [31] where a task reward function is inferred from the demonstrations.”
Zeng, H., Gu, Y., Liu, X., Huang, H., Pan, M., & Liu, T. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3672883
“Gaussian process regression [22] and Gaussian mixture model / regression (GMM-GMR) [23] can reproduce the joint angles learned from the demonstration database.”
SafeHumanoid: VLM-RAG-Driven Impedance Control of Humanoid Robot
Mahmoud, Y., Sam, J., Nguyen, K., Fernando, M. J., Tokmurziyev, I., Cabrera, M. A., Khan, M. H., Lykov, A., & Tsetserukou, D. (2026) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3776734.3794539
Fast reprogramming and adaptive reproduction of contact-rich assembly
Rakovitis, D., Origanti, V. K., Bargsten, V., Danzglock, A., Mronga, D., & Kirchner, F. (2026) · Frontiers in Robotics and AI · doi:10.3389/frobt.2026.1746577 · Cited by 1
“…hours to days (Luo et al., 2019; Tang et al., 2023; Noseworthy et al., 2025; Guo et al., 2025; Wu et al., 2025; Yan et al., 2021; Jha et al., 2022; Calinon, 2016; Paraschos et al., 2013; Kramberger et al., 2017), or additional multiple real-world trials (≈ 10 − 30 ) for transfer learning…”
Contributions to shared control architectures for advanced telemanipulation
Abi-Farraj, F. (2018) · doi:10.70675/9e0440c1zf029z42e1za504z244c5cea5578
“In this case, we can model the conditional distribution of the demonstrated trajectories given the context in order to generalize the demonstrated trajectories to new situations [210, 215, 216].”
BackgroundMicro-Data Reinforcement Learning for Adaptive Robots
Chatzilygeroudis, K. I. (2018) · theses.fr (ABES) · doi:10.70675/7990d9edz7f40z4344z9318z6da4e46000c0
“BACKGROUND 30 dimensionality (Calinon, 2016), but these models have only been applied to learning from demonstration applications so far.”
BackgroundMethodDermy, O. (2018) · theses.fr (ABES) · doi:10.70675/66c4dacezf631z43bez873cz81160a1d5dda
“pbdlib-matlab HMM, GMM and others [39] Matlab Baxter [35]”
Maestre, C. (2018) · theses.fr (ABES) · doi:10.70675/d5cf82abzf520z4560z9724zb53c5eb4a78b
“The states are built using raw data directly demonstrated to a physical Baxter robot by an external agent, i.e. learning from demonstration (Billard and Calinon (2016)).”
MethodLearning Surgical Gestures from Data
Ginesi, M. (2026) · Springer tracts in advanced robotics · doi:10.1007/978-3-032-04247-7_5
Object-centric task representation and transfer using diffused orientation fields
Bilaloglu, C., Löw, T., & Calinon, S. (2026) · Science Robotics · doi:10.1126/scirobotics.aea1762
Self-citationYe, C., Wu, C., Luo, M., et al. (2026) · Biomimetics · doi:10.3390/biomimetics11050351
“Nevertheless, TP-GMM relies on human-defined frames, which may be redundant or omit critical task constraints [21].”
Xu, Z., Zou, Y., & Zhang, J. (2026) · Signal Image and Video Processing · doi:10.1007/s11760-026-05379-2
Design and Evaluation of AR-Based Real-Time Feedback System for Kinesthetic Robot Teaching
Bilal, M., Ratnayake, T. S., Chacon, D. A., Lipovetzky, N., Oetomo, D., & Johal, W. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3812951
“For the learning algorithm, we used a standard Gaussian Mixture Model (GMM) to train LfD models from the collected data [17, 19].”
Stage-aware and roughness-constrained diffusion policy for multi-stage robotic polishing
Ke, S., Zhang, J., Zhao, H., et al. (2026) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2026.103417
Data augmentation and reactive adaptation for robot trajectory learning from demonstrationPreprint
Burlizzi, R., Vochten, M., Aertbeliën, E., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7021521
Sampling-Based Motion Planning Using DMPs Under a Novel Scaling Approach
Chen, J., Wang, Y., Takamido, R., et al. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3711877
“GMR-RRT*[16], for instance, employs Gaussian Mixture Regression (GMR) [17] to learn a distribution over demonstrated trajectories and uses it to bias the sampling process of RRT*.”
Learning a Unified Dynamic System Model Across Diverse Robotic Demonstration Tasks
Jin, Z., Si, W., Xu, R., Yang, C., & Lv, C. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3718280
“Note that previous TP approaches are combined with time-variant models, such as TP-GMM [14], [15], [16] and TP-DMP [17], [18], [19], and thus su ff er from the shortcomings such as weak robustness caused by the o ffl ine setting.”
Li, W., Zaatari, S. E., Wang, Y., et al. (2026) · Springer series in advanced manufacturing · doi:10.1007/978-3-032-20817-0_6
Adaptive Obstacle Avoidance in Trajectory Planning of Collaborative Robots for Dynamic Manufacturing
Li, W., Hu, Y., Wang, Y., Hu, K., & Pham, D. T. (2026) · Springer series in advanced manufacturing · doi:10.1007/978-3-032-20817-0_12
Collaborative Robot Technologies for Industrial Tasks
Li, W., Zaatari, S. E., Marei, M., Usman, Z., & Pham, D. T. (2026) · Springer series in advanced manufacturing · doi:10.1007/978-3-032-20817-0_2
Learning from Demonstration for Autonomous Generation of Robot Trajectories
Li, W., Wang, Y., Liang, Y., & Pham, D. T. (2026) · Springer series in advanced manufacturing · doi:10.1007/978-3-032-20817-0_3
Optimised Learning from Demonstration for Collaborative Robots
Li, W., Wang, Y., Hu, Y., Zaatari, S. E., Zhou, Y., & Pham, D. T. (2026) · Springer series in advanced manufacturing · doi:10.1007/978-3-032-20817-0_7
Li, C., Xian, L., Liu, S., et al. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3730290
“1) Joint Reference Trajectory Model: A joint reference trajectory model was established using Gaussian Mixture Models (GMM) and Gaussian Mixture Regression (GMR) [16].”
Li, W.-D., Qian, C., Luo, W., et al. (2026) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2026.103431
Bajrami, A., Palpacelli, M.-C., Suomalainen, M., et al. (2026) · doi:10.1109/mesa70578.2026.11684290
“Waypoint and keyframe representations provide a compact interface between demonstration and execution [11], [17]– [19].”
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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