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A tutorial on task-parameterized movement learning and retrieval
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“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]
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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).”
InfluentialVariable Impedance Control and Learning—A Review
Abu‐Dakka, F. J., & Saveriano, M. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.590681 · Cited by 220
“They compared their results with the traditional Euclidean-based GMM/GMR (Calinon, 2016) after reparameterizing stiffness matrices using Cholesky decomposition.”
ResultUsing probabilistic movement primitives in robotics
Paraschos, A., Daniel, C., Peters, J., & Neumann, G. M. (2017) · Autonomous Robots · doi:10.1007/s10514-017-9648-7 · Cited by 205
“An extension of the approach (Calinon 2016) enabled generalization to different situations by recording the movement from different spaces and tracking the affine transformation to each space.”
BackgroundMethodInfluentialA Learning Framework of Adaptive Manipulative Skills From Human to Robot
Yang, C., Zeng, C., Cong, Y., Wang, N., & Wang, M. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2826064 · Cited by 175
“One may also utilize some statistical approaches to encode the multiple profiles (see, e.g., [31]).”
BackgroundA survey of robot manipulation in contact
Suomalainen, M., Karayiannidis, Y., & Kyrki, V. (2022) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2022.104224 · Cited by 171
“A straightforward solution is the use of Gaussian Mixture Regression (GMR), which first builds a joint distribution of the Gaussians and then derives the regression function from the joint distribution [193, 194].”
BackgroundMethodA Survey on Policy Search Algorithms for Learning Robot Controllers in a Handful of Trials
Chatzilygeroudis, K., Vassiliades, V., Stulp, F., Calinon, S., & Mouret, J.-B. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2019.2958211 · Cited by 160
Self-citationSilva, N., Zhang, D., Kulvičius, T., Gail, A., Barreiros, C., Lindstaedt, S., Kraft, M., Bölte, S., Poustka, L., Nielsen‐Saines, K., et al. (2021) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2021.103854 · Cited by 137
GMR-RRT*: Sampling-Based Path Planning Using Gaussian Mixture Regression
Wang, J., Li, T., Li, B., & Meng, M. Q. ‐. (2022) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2022.3150748 · Cited by 114
“This methodutilizestheGaussianMixtureRegression(GMR)[10] toinferrobotmotionsfromthehumandemonstratedtrajecto-riesforitsefficientimplementationandeasycombinationwith probabilisticsampling-basedplanners.”
An Approach for Imitation Learning on Riemannian Manifolds
Zeestraten, M. J. A., Havoutis, I., Silvério, J., Calinon, S., & Caldwell, D. G. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2657001 · Cited by 109
“A popular regression technique for Euclidean GMM is Gaussian Mixture Regression (GMR) [4].”
BackgroundMethodInfluentialSelf-citationA review on manipulation skill acquisition through teleoperation‐based learning from demonstration
Si, W., Wang, N., & Yang, C. (2021) · Cognitive Computation and Systems · doi:10.1049/ccs2.12005 · Cited by 109
“[70], which in essence models local (or relative) trajectories and corresponding local patterns, therefore endowing GMM with better extrapolation performance.”
BackgroundGaussians on Riemannian Manifolds: Applications for Robot Learning and Adaptive Control
Calinon, S. (2020) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2020.2980548 · Cited by 101
“Figure 9 presents an overview of this application (also see [41] for a description of this teleoperation approach and [39] for a general description of TP-GMM).”
BackgroundMethodInfluentialSelf-citationLearning adaptive dressing assistance from human demonstration
Pignat, E., & Calinon, S. (2017) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2017.03.017 · Cited by 87
“Most learning by demonstration approaches model a direct dependence between temporal and spatial data, with either radial basis functions (RBFs) [16] or Gaussian mixture regression (GMR) [1].”
MethodResultInfluentialSelf-citationLearning Robot Manipulation Tasks With Task-Parameterized Semitied Hidden Semi-Markov Model
Tanwani, A. K., & Calinon, S. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2016.2517825 · Cited by 84
“Task-parameterized GMMs in [14], [22], [23] can handle new environmental situations by defining external frames of reference.”
BackgroundSelf-citationA review of recent trend in motion planning of industrial robots
Tamizi, M. G., Yaghoubi, M., & Najjaran, H. (2023) · International Journal of Intelligent Robotics and Applications · doi:10.1007/s41315-023-00274-2 · Cited by 83
Robot Cooking With Stir-Fry: Bimanual Non-Prehensile Manipulation of Semi-Fluid Objects
Liu, J., Chen, Y.-T., Dong, Z., Wang, S., Calinon, S., Li, M., & Chen, F. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3153728 · Cited by 90
“There are already some skill learning solutions for single robot arm, such as Dynamic Movement Primitives (DMPs) [7], Probabilistic Movement Primitives (ProMPs) [8] and Task-parameterized Gaussian Mixture Model (TP-GMM) [9].”
Self-citationLearning task-parameterized dynamic movement primitives using mixture of GMMs
Pervez, A., & Lee, D. (2017) · Intelligent Service Robotics · doi:10.1007/s11370-017-0235-8 · Cited by 75
“A Cartesian impedance controller with a control frequency of 100Hz is used for motion generation.”
BackgroundMethodThe Classification and New Trends of Shared Control Strategies in Telerobotic Systems: A Survey
Li, G., Li, Q., Yang, C., Su, Y., Zuqiang, Y., & Wu, X. (2023) · IEEE Transactions on Haptics · doi:10.1109/toh.2023.3253856 · Cited by 75
“…[118], theHidden(Semi-)MarkovModel(HMM/HSMM)[119],[120], theDynamicMovementPrimitives(DMP)[121],theStableEs-timatorofDynamicalSystems(SEDS)[122],theProbabilistic Movement Primitives (ProMP) [123], the Task-Parameterized GMM (TPGMM) [124], and the Kernelized Movement Prim-itives (KMP) [125], etc.”
BackgroundOvercoming some drawbacks of Dynamic Movement Primitives
Ginesi, M., Sansonetto, N., & Fiorini, P. (2021) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2021.103844 · Cited by 69
Birk, A., Antonelli, G., Lillo, P. D., et al. (2018) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2018.2869523 · Cited by 68
Self-citationLearning from demonstration for semi-autonomous teleoperation
Havoutis, I., & Calinon, S. (2018) · Autonomous Robots · doi:10.1007/s10514-018-9745-2 · Cited by 63
“GMM/GMR have been successful in representing sets of demonstrated examples that are time-indexed, this way using time as the Gaussian conditioning variable to perform the regression, see Calinon (2016) for an overview.”
BackgroundMethodInfluentialSelf-citationA generative model for intention recognition and manipulation assistance in teleoperation
Tanwani, A. K., & Calinon, S. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202136 · Cited by 63
“and then computing the products of the linearly transformed Gaussians for each component [29], [4] with”
BackgroundSelf-citationToward Orientation Learning and Adaptation in Cartesian Space
Huang, Y., Abu‐Dakka, F. J., Silvério, J., & Caldwell, D. G. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.3010633 · Cited by 63
“It is worth mentioning that, given the joint probability distribution P(s, ξ) in (32), GMR can be employed to predict the corresponding output for a query input s∗ through calculating P(ξ|s∗) .”
BackgroundMethodResultInfluentialTowards Robust Skill Generalization: Unifying Learning from Demonstration and Motion Planning.
Rana, M. A., Mukadam, M., Ahmadzadeh, S. R., Chernova, S., & Boots, B. (2017) · Conference on Robot Learning · Cited by 70
Learning from Demonstration (Programming by Demonstration)
Calinon, S. (2018) · Springer eBooks · doi:10.1007/978-3-642-41610-1_27-1 · Cited by 66
“This can also be incorporated more directly within a control strategy by retrieving a controller following a minimal intervention strategy [43, 6, 47].”
BackgroundSelf-citationGuiding Trajectory Optimization by Demonstrated Distributions
Osa, T., E., A. M. G., Stolkin, R., Lioutikov, R., Peters, J., & Neumann, G. M. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2653850 · Cited by 58
Learning Barrier Functions for Constrained Motion Planning with Dynamical Systems
Saveriano, M., & Lee, D. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967981 · Cited by 61
“DMPs can plan in joint or Cartesian space [13], [14] and can be parameterized to customize the execution [15], [16].”
BackgroundRobust and adaptive door operation with a mobile robot
Arduengo, M., Torras, C., & Sentis, L. (2021) · Intelligent Service Robotics · doi:10.1007/s11370-021-00366-7 · Cited by 63
Intuitive Task-Level Programming by Demonstration Through Semantic Skill Recognition
Steinmetz, F., Nitsch, V., & Stulp, F. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2928782 · Cited by 58
“and Gaussian Mixture Models (GMMs) [6].”
Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation
Frank, F., Paraschos, A., van der Smagt, P., et al. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3127108 · Cited by 56
“In [49] TP-GMM is extended to include frames representing Cartesian orientation and the authors apply their method to a bimanual setting.”
MethodOpen source Vicon Toolkit for motion capture and Gait Analysis
Goldfarb, N., Lewis, A., Tacescu, A., et al. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106414 · Cited by 55
A review on interaction control for contact robots through intent detection
Li, Y., Sena, A., Wang, Z., Xing, X., Babič, J., van Asseldonk, E. H. F., & Burdet, E. (2022) · Progress in Biomedical Engineering · doi:10.1088/2516-1091/ac8193 · Cited by 54
“In the case of [58], for a peg-in-hole type task a minimal intervention controller is defined based on a learned task representation that results in the robot behaving compliantly during free-space movements while moving toward the hole, then engaging in a task-directed stiffness as it performs the insertion stage of…”
BackgroundInfluentialLearning Task Priorities from Demonstrations
Silvério, J., Calinon, S., Rozo, L., et al. (2018) · IEEE Transactions on Robotics · doi:10.1109/tro.2018.2878355 · Cited by 55
“Our approach takes task-parameterized Gaussian mixture models (TP-GMM) [8], [10] (reviewed in Section III) as an example, which was originally used to probabilistically encode Cartesian end-effector motions from the perspective of different coordinate systems.”
BackgroundMethodInfluentialSelf-citationDemonstration based trajectory optimization for generalizable robot motions
Koert, D., Maeda, G. J., Lioutikov, R., Neumann, G. M., & Peters, J. (2016) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2016.7803324 · Cited by 49
“The particular aspect of motion planning and obstacle avoidance in combination with movement primitives and human demonstrations has been addressed for specific cases such as, Dynamic Movement Primitives [24], [25], a mixture of dynamical systems [26] or Task-Parameterized Gaussian Mixture Models [27].”
BackgroundA learning-based shared control architecture for interactive task execution
Abi-Farraj, F., Osa, T., Pedemonte, N., Neumann, G. M., & Giordano, P. R. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989042 · Cited by 52
“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 [11], [24], [25].”
Merging Position and Orientation Motion Primitives
Saveriano, M. (n.d.) · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · Cited by 54
Generalized Task-Parameterized Skill Learning
Huang, Y., Silvério, J., Rozo, L., & Caldwell, D. G. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8461079 · Cited by 49
“Note that intuitive insights on this variant have been studied for two frames [11] and multiple frames [12], while here we aim to provide a mathematical proof.”
BackgroundConditional Neural Movement Primitives
Seker, M. Y., Imre, M., Piater, J., & Uğur, E. (2019) · Robotics: Science and Systems · doi:10.15607/rss.2019.xv.071 · Cited by 53
“Among others, learning methods that are based on dynamic systems [19] and statistical modeling [5] have been popular in the recent years.”
BackgroundMerging Position and orientation Motion Primitives
Saveriano, M., Franzel, F., & Lee, D. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793786 · Cited by 48
Quantifying teaching behavior in robot learning from demonstration
Sena, A., & Howard, M. (2019) · The International Journal of Robotics Research · doi:10.1177/0278364919884623 · Cited by 46
“While there are many learning methods which are suitable for this step, TP-GMM is chosen as it has been shown to be particularly effective in generalizing from a limited set of demonstrations to unseen conditions.”
InfluentialData Driven Models for Human Motion Prediction in Human-Robot Collaboration
Li, Q., Zhang, Z., You, Y., Mu, Y., & Feng, C. (2020) · IEEE Access · doi:10.1109/access.2020.3045994 · Cited by 47
“For endowing GMM with better extrapolation performance, a task-parameterized formulation was studied in [15], which essentially models local trajectories and corresponding local patterns.”
MethodLillo, P. D., Simetti, E., Wanderlingh, F., et al. (2020) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2020.2971440 · Cited by 41
“The Cognitive Engine relies on a parameterized Gaussian mixture model (GMM) [31] in order to encode demonstrations and generalize the tasks to”
MethodMemory of Motion for Warm-Starting Trajectory Optimization
Lembono, T. S., Paolillo, A., Pignat, E., & Calinon, S. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2972893 · Cited by 43
“Gaussian Mixture Regression (GMR) is an example of such local models approaches [22].”
BackgroundSelf-citationGesture Knitter: A Hand Gesture Design Tool for Head-Mounted Mixed Reality Applications
Mo, G. B., Dudley, J. J., & Kristensson, P. O. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445766 · Cited by 46
“Many other parametric probabilistic methods have been applied to related problems [2, 3, 10, 11, 18, 44] but are not well suited to our task.”
MethodCombining Imitation Learning With Constraint-Based Task Specification and Control
Vergara, C., Schutter, J. D., & Aertbeliën, E. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2898035 · Cited by 38
“This is the case for task parameterized GMM (TP-GMM) [16] and task parameterized DMP (TP-DMP) [17], which parameterize relevant aspects of the task such as poses at the initial and end location of a trajectory, different reference frames, obstacles in between etc. Dedicated controllers have been developed to tailor th…”
BackgroundMethodInfluentialOptimised Learning from Demonstrations for Collaborative Robots
Wang, Y., Hu, Y. D., Zaatari, S. E., Li, W., & Zhou, Y. Q. (2021) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2021.102169 · Cited by 38
Performance Evaluation of Optical Motion Capture Sensors for Assembly Motion Capturing
Hu, H., Cao, Z., Yang, X., Xiong, H., & Lou, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3074260 · Cited by 36
Compliant Parametric Dynamic Movement Primitives
Uğur, E., & Girgin, H. (2019) · Robotica · doi:10.1017/s026357471900078x · Cited by 36
“In this subsection, we compare the capabilities of the underlying PHMM representation of CPDMP with a parametric representation that also encodes the statistical information of multiple trajectories, namely Task Parameterized Gaussian Markov Mixteure Models (TPGMM).(32) Assume that two sets of trajectories that pass a…”
MethodBagging for Gaussian mixture regression in robot learning from demonstration
Ye, C., Yang, J., & Ding, H. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01686-8 · Cited by 35
Duan, A., Victorova, M., Zhao, J., Sun, Y., Zheng, Y., & Navarro-Alarcón, D. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3186504 · Cited by 38
“Furthermore, regarding acquisition of variable impedance gains, we follow the general paradigm of learning by demonstration [22].”
BackgroundInfluentialLiao, Z., Jiang, G., Zhao, F., Wu, Y., Yue, Y., & Mei, X. (2022) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2022.3212707 · Cited by 36
“…transferring approaches have been introduced, such as the dynamic movement primitive (DMP) [9], Gaussian mixture model [10], probabilistic movement primitive [11], task-parameterized Gaussian mixture model [12], stable estimator of dynamical system [13], and kernelized movement primitive [14].”
MethodZhou, L., Li, Z., Li, Y., et al. (2025) · IEEE Transactions on Robotics · doi:10.1109/tro.2025.3600125 · Cited by 35
“GMR [40], [41], [42] provides the theoretical framework for this transition.”
Adaptive obstacle avoidance in path planning of collaborative robots for dynamic manufacturing
Hu, Y., Wang, Y., Hu, K., & Li, W. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01825-9 · Cited by 35
“More specifically, the approach can be directly used to support robots with a LfD ability via visual or kinaesthetic learning.”
MethodInfluentialLearning Task-Parameterized Skills From Few Demonstrations
Zhu, J., Gienger, M., & Kober, J. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3150013 · Cited by 34
Jin, Z., Si, W., Liu, A., Zhang, W., Yu, L. J., & Yang, C. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3303011 · Cited by 37
“In [13], [14], [15], task-parameterized GMM (TP-GMM) was presented to further enhance the generalization capacity by including task-related parameters in the EM optimization procedure.”
MethodSafe Q-Learning Method Based on Constrained Markov Decision Processes
Ge, Y., Zhu, F., Ling, X., & Liu, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2952651 · Cited by 35
“regarded that the integrated external information was not used to guide the agent to explore, but to deduce a safety policy offline [42], [43].”
BackgroundAccelerated Sensorimotor Learning of Compliant Movement Primitives
Petrič, T., Gams, A., Colasanto, L., Ijspeert, A. J., & Ude, A. (2018) · IEEE Transactions on Robotics · doi:10.1109/tro.2018.2861921 · Cited by 32
“[20] S. Calinon, T. Alizadeh, and D. Caldwell, “On improving the extrapolation capability of task-parameterized movement models,” in Proc.”
BackgroundMethodInfluentialCanal, G., Pignat, E., Alenyà, G., Calinon, S., & Carme Torras, A. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8460606 · Cited by 33
“As proposed in [19], the environment will be defined by several coordinate systems in which the data will be projected.”
BackgroundSelf-citationRobot skill learning in latent space of a deep autoencoder neural network
Pahič, R., Lončarević, Z., Gams, A., & Ude, A. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103690 · Cited by 33
Robot trajectory tracking control using learning from demonstration method
Xu, S., Ou, Y., Duan, J., Wu, X., Feng, W., & Liu, M. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.01.052 · Cited by 32
Imitation and mirror systems in robots through Deep Modality Blending Networks
Seker, M. Y., Ahmetoğlu, A., Nagai, Y., Asada, M., Öztop, E., & Uğur, E. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.11.004 · Cited by 33
Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints
Silvério, J., Huang, Y., Rozo, L. D., Calinon, S., & Caldwell, D. G. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594103 · Cited by 30
“Two popular approaches are GMM, combined with Gaussian Mixture Regression [20], and GPR [15].”
MethodSelf-citationEncoding Multiple Sensor Data for Robotic Learning Skills From Multimodal Demonstration
Zeng, C., Yang, C., Zhong, J., & Zhang, J. (2019) · IEEE Access · doi:10.1109/access.2019.2945484 · Cited by 31
Human Intention Inference and On-Line Human Hand Motion Prediction for Human-Robot Collaboration
Luo, R. C., & Mai, L. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968192 · Cited by 32
“works proposed in [28,32,33] did successfully applied to robot, however, the way they adapt to the new environment may not be the same as how human react to the new environment.”
BackgroundLearning Gait Models With Varying Walking Speeds
Zou, C., Huang, R., Cheng, H., & Qiu, J. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3006818 · Cited by 31
“The distribution of joint angles can be learned by GMM with 10 Gaussian components, the reference trajectory can be reproduced by GMR.”
MethodInfluentialA workload adaptive haptic shared control scheme for semi-autonomous driving
Luo, R., Weng, Y., Wang, Y., Jayakumar, P., Brudnak, M., Paul, V., Desaraju, V. R., Stein, J. L., Ersal, T., & Yang, X. J. (2021) · Accident Analysis & Prevention · doi:10.1016/j.aap.2020.105968 · Cited by 30
“The parameters of the HMMs were learned by the Expectation Maximization algorithm using the open source implementations from [21], [22].”
MethodCalinon, S., & Lee, D. (2018) · Springer eBooks · doi:10.1007/978-94-007-6046-2_68 · Cited by 41
Self-citationA probabilistic framework for learning geometry-based robot manipulation skills
Abu‐Dakka, F. J., Huang, Y., Silvério, J., & Kyrki, V. (2021) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2021.103761 · Cited by 30
“Both forces and stiffness profiles were probabilistically encoded using two different approaches: geometry-aware GMM/GMR [12] and Euclidean-based GMM/GMR [26].”
InfluentialPrediction of Intention during Interaction with iCub with Probabilistic Movement Primitives
Dermy, O., Paraschos, A., Ewerton, M., Peters, J., Charpillet, F., & Ivaldi, S. (2017) · Frontiers in Robotics and AI · doi:10.3389/frobt.2017.00045 · Cited by 29
Zaatari, S. E., Wang, Y., Li, W., et al. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2020.102109 · Cited by 27
Sena, A., Michael, B., & Howard, M. (2019) · 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967688 · Cited by 27
“Significant improvement using the proposed method is shown in a test data set for a reaching task, versus the original TP-GMR method described in [4] and a modified TP method described in [5], specifically designed to improve extrapolation of learned skills.”
BackgroundMethodInfluentialShavit, Y., Figueroa, N., Salehian, S. S. M., & Billard, A. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2833497 · Cited by 26
“task space [6], [7] via probabilistic models.”
One-shot Learning from Demonstration Approach Toward a Reciprocal Sign Language-based HRI
Hosseini, S. R., Taheri, A., Alemi, M., & Meghdari, A. F. (2021) · International Journal of Social Robotics · doi:10.1007/s12369-021-00818-1 · Cited by 26
Anthropomorphic Reaching Movement Generating Method for Human-Like Upper Limb Robot
He, C., Xu, X., Zheng, X., Xiong, C., Li, Q., Chen, W., & Sun, B.-Y. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3107341 · Cited by 24
“In this article, we use the task-parametrization GMM (TP-GMM) [37] in the joint space to encode the demonstration movements.”
MethodGeneralized Orientation Learning in Robot Task Space
Huang, Y., Abu‐Dakka, F. J., Silvério, J., & Caldwell, D. G. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793540 · Cited by 25
“Many results of imitation learning have been reported in the past few years, such as dynamic movement primitives (DMP) [5], probabilistic movement primitives (ProMP) [6], task-parameterized Gaussian mixture model (TP-GMM) [7], [8] and kernelized movement primitives (KMP) [9].”
BackgroundMethodInfluentialZaatari, S. E., Wang, Y., Hu, Y., et al. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01743-w · Cited by 23
“More details of the process are depicted below (Calinon 2016): • A human operator demonstrates a path that a cobot should follow for picking an object from a start point and drop the object into an end-point.”
BackgroundLearning Forceful Manipulation Skills from Multi-modal Human Demonstrations
Le, A. T., Guo, M., van Duijkeren, N., Rozo, L. D., Krug, R., Kupcsik, A., & Bürger, M. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636828 · Cited by 27
“Compared with traditional motion planning [10], Learning from Demonstration (LfD) is an intuitive and effective way to transfer human skills to robots [4], [7], [11].”
BackgroundMethodInfluentialMachine Learning Approaches for Motor Learning: A Short Review
Caramiaux, B., Françoise, J., Liu, W., Sanchez, T., & Bevilacqua, F. P. (2020) · Frontiers in Computer Science · doi:10.3389/fcomp.2020.00016 · Cited by 21
“[16] proposed such approach in robotics to adapt the robot movement parameters when new target coordinates are set for the robot arm.”
InfluentialA Framework for Predicting Haptic Feedback in Needle Insertion in 5G Remote Robotic Surgery
Boabang, F., Glitho, R., Elbiaze, H., Belqami, F., & Alfandi, O. (2020) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc46108.2020.9045432 · Cited by 21
“Our evaluation was done on the test set based on Calinon Matlab codes in [15].”
MethodPignat, E., Silvério, J., & Calinon, S. (2021) · The International Journal of Robotics Research · doi:10.1177/02783649211040561 · Cited by 24
“They are often used to define motions by introducing a dependence to time Calinon and Billard (2009), as observation models in hidden Markov models Calinon (2016), or transformed by a timedependent basis matrix Paraschos et al. (2017). Most of these works learn the different models separately and combine them at the c…”
BackgroundMethodInfluentialSelf-citationTask-Adaptive Robot Learning From Demonstration With Gaussian Process Models Under Replication
Arduengo, M., Colomé, A., Borràs, J., Sentis, L., & Torras, C. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3056367 · Cited by 22
“On the one hand, ProMP and GMM involve an Expectation-Maximization algorithm.”
BackgroundInfluentialA Learning-Based Object Tracking Strategy Using Visual Sensors and Intelligent Robot Arm
Xu, S., Chen, K., Ou, Y., Wang, Z., & Yang, C. (2022) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2022.3213730 · Cited by 22
Biagiotti, L., Meattini, R., Chiaravalli, D., Palli, G., & Melchiorri, C. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3258669 · Cited by 22
“…as presented, with the exception of pagination. or NURBS [7], or on different types of probabilistic methods, e.g., Gaussian mixture regression [8], [9], dynamical movement primitives (DMPs) [10], probabilistic movement primitives [11], kernelized movement primitives [12], or Gaussian process…”
MethodLeutert, F., Bohlig, D., Kempf, F., Schilling, K., Mühlbauer, M., Ayan, B., Hulin, T., Stulp, F., Albu-Schäffer, A., Kutscher, V., et al. (2024) · Acta Astronautica · doi:10.1016/j.actaastro.2024.01.019 · Cited by 26
“In future work, we intend to use tools from probability theory [58, 59, 60] to take perceptual uncertainty into account to arbitrate between fixtures [61] which also require novel passivity-based approaches to ensure stability.”
MethodJaquier, N., & Calinon, S. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202138 · Cited by 22
Self-citationLearning Stable Vector Fields on Lie Groups
Urain, J., Tateo, D., & Peters, J. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3219019 · Cited by 21
“During the last two decades, there has been vast research on learning policy architectures [3], [4], [5], [6], [7] that guarantee a set of desirable inductive biases.”
BackgroundIncremental imitation learning of context-dependent motor skills
Ewerton, M., Maeda, G. J., Kollegger, G., et al. (2016) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2016.7803300 · Cited by 21
“Other methods such as task parameterized models discussed in [11] and [12] have very good generalization properties as well.”
MethodAccelerating Interactive Reinforcement Learning by Human Advice for an Assembly Task by a Cobot
Winter, J. D., Beir, A. D., Makrini, I. E., et al. (2019) · Robotics · doi:10.3390/robotics8040104 · Cited by 21
Gaussian-process-based robot learning from demonstration
Arduengo, M., Colomé, A., Lobo-Prat, J., Sentis, L., & Carme Torras, A. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-023-04551-7 · Cited by 21
“…relevant contributions, the following methods can be highlighted: Dynamic Movement Primitives (DMP) [3,4], Probabilistic Movement Primitives (ProMP) [5], Gaussian Mixture Model-Gaussian Mixture Regression (GMM-GMR) [6,7], Kernelized Movement Primitives [8,9], and Gaussian Processes (GP) [10,11].”
BackgroundMethodInfluentialQuantifying Demonstration Quality for Robot Learning and Generalization
Sakr, M., Li, Z. J., der Loos, H. F. M. V., Kulić, D., & Croft, E. A. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3191950 · Cited by 20
“Calinon [17] provides more in-depth detail for this approach.”
BackgroundLi, W., Wang, Y., Liang, Y., & Pham, D. T. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102625 · Cited by 21
Learning Trajectory Distributions for Assisted Teleoperation and Path Planning
Ewerton, M., Arenz, O., Maeda, G. J., et al. (2019) · Frontiers in Robotics and AI · doi:10.3389/frobt.2019.00089 · Cited by 20
“In that work, task-parameterized Gaussian Mixture Models (TP-GMMs) (Calinon, 2016) have been used to encode the probability distribution of demonstrated trajectories.”
MethodResultInfluentialA robot calligraphy writing method based on style transferring algorithm and similarity evaluation
Liang, D., Liang, D., Liang, D., et al. (2019) · Intelligent Service Robotics · doi:10.1007/s11370-019-00298-3 · Cited by 20
Learning and Sequencing of Object-Centric Manipulation Skills for Industrial Tasks
Rozo, L. D., Guo, M., Kupcsik, A., Todescato, M., Schillinger, P., Giftthaler, M., Ochs, M., Spies, M., Waniek, N., Kesper, P., et al. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341570 · Cited by 21
“Expectation-Maximization (EM) [8] is a well-established method to learn such models.”
MethodInfluentialA Task-Learning Strategy for Robotic Assembly Tasks from Human Demonstrations
Ding, G., Liu, Y., Zang, X., et al. (2020) · Sensors · doi:10.3390/s20195505 · Cited by 19
“Generally, movement primitive learning methods fall into two groups: one is based on probabilistic models [11,19,20], the other is based on dynamical systems [21,22].”
BackgroundProbabilistic Surface Friction Estimation Based on Visual and Haptic Measurements
Le, T. N., Verdoja, F., Abu-Dakka, F. J., et al. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3062585 · Cited by 19
“(2) After the model M is fitted to a given dataset, by using Gaussian Mixture Regression (GMR) [19], we can estimate the haptic feature Hi at each data point i = 1, . . ., N in the unexplored regions given input Vi by means of the conditional probability P(H | V) expressed as P(Hi | Vi) ∼ C∑ c=1 hc(Vi)N (μ̂Hc (Vi), Σ̂…”
MethodInfluentialADAM: a robotic companion for enhanced quality of life in aging populations
Mora, A., Prados, A., Méndez, A., Espinoza, G., González, P., Lopez, B., Muñoz, V., Moreno, L. E., Garrido, S., & Barber, R. (2024) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2024.1337608 · Cited by 20
“To achieve this, a combination of the FML algorithm and Task Parameterized Gaussian Mixture Models (TPGMM) has been employed (Calinon, 2016).”
Method
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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