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REVIEW 4 major objections 5 minor 2 cited by

Movement Primitives in Robotics: A Comprehensive Survey

T0 review · 4 major / 5 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read This survey claims to provide an encyclopedic, chronological map of over 700 papers on movement primitives in robotics, organized into five core framework families and their applications.

desk verdict A useful but non-encyclopedic survey: the curation is real value, the corpus is undocumented, and negative claims should be softened until a search protocol is added. read the letter →

arxiv 2601.02379 v2 pith:5DLJPXHJ submitted 2025-12-17 cs.RO cs.AI

Nolan B. Gutierrez , Joseph M. Cloud , William J. Beksi This is my paper · ORCID
classification cs.ROcs.AI
keywords movementprimitivesrobotlearningfromdemonstrationdynamicprobabilistickernelizedconditionalneuralroboticssurvey
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The survey sets out to be the reference map for movement primitives: reusable trajectory-level building blocks robots learn from human demonstrations. It claims to cover over 700 papers from roughly three decades, untangle the historical development of this research area, and organize it into five core framework families—dynamic, probabilistic, kernelized, conditional-neural, and Fourier movement primitives. A sympathetic reader would take from it that a practitioner can now compare frameworks on shared axes (single vs. multiple demonstrations, discrete vs. rhythmic motion, parameter tuning, computational scaling) and see where the field has and has not been explored. The payoff would be a faster route from a new robotic task to a suitable framework and its known extensions.

What carries the argument

The organizing device is the framework-level taxonomy: each movement-primitive family is characterized by its demonstration input (single or multiple, manually aligned or not), its movement encoding (differential equation, probability distribution, kernel, neural network, or Fourier series), and its generated output (open- or closed-loop, discrete or rhythmic, adaptable). The chronological ordering and a five-way comparison table do the work of making movement primitives a navigable field rather than a tangle of group-specific names. A second mechanism is the application taxonomy, which groups hundreds of papers into task categories such as assembly, insertion, grasping, surgery, agricultura

What would settle it

Run a documented, reproducible literature search for movement-primitive frameworks and extensions; if it retrieves a major framework or extension family absent from the survey—for example, any published extension of Fourier movement primitives predating the survey—the completeness claim and the Section 7.2 'no extensions' statement are falsified.

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Extended reading notes

Core claim

The central claim is that the movement-primitive literature, though intertwined and difficult to follow, can be systematically disentangled by chronological organization and a taxonomy built on three distinguishing criteria: how demonstrations are input, how movements are encoded, and what output trajectories look like. On that basis the survey identifies five principal frameworks—DMPs (spring-damper attractor systems with a learned forcing term), ProMPs (distributions over trajectory weights), KMPs (kernel-based nonparametric probabilistic regression), CNMPs (neural-process priors), and FMPs (Fourier-domain periodic primitives)—and maps their applications across contact manipulation, field

Load-bearing premise

The load-bearing premise is that the surveyed corpus of over 700 papers is complete and representative; because the survey gives no search protocol or inclusion criteria, any omitted lineage or extension family would silently skew the taxonomy, comparisons, and gap claims.

Editorial extensions

If this is right

  • A robotics practitioner facing a new skill-learning task can use the survey to shortlist frameworks: DMPs for one-shot discrete movements, ProMPs when multiple demonstrations and variability matter, KMPs for high-dimensional inputs, CNMPs for conditioning on external stimuli, and FMPs for rhythmic tasks.
  • Comparing frameworks on shared axes makes the core trade-offs explicit—number of demonstrations, manual alignment, parameter tuning, computational scaling—so choices can be justified rather than inherited from a lab's convention.
  • The application tables show where movement primitives have been successfully deployed and where activity is thin, which can steer new work toward under-explored combinations such as FMPs outside rhythmic contact tasks.
  • The survey's negative finding that FMPs have no direct extensions to date identifies a concrete research gap within the field as the authors map it.
  • The curated software list lowers the barrier to entry, making it feasible for a new group to reproduce and extend a given framework.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The five-family taxonomy is itself a claim about the field's deep structure; Section 9 admits that other dynamical-systems, probabilistic, and reinforcement-learning approaches exist, so whether the five-family canon holds is a judgment the corpus alone cannot fully prove.
  • The 'no FMP extensions' statement is a negative claim that depends entirely on the completeness of the literature search; because no search protocol is given, a reader should treat it as a provisional gap rather than an established absence.
  • If the living software repository grows, this survey could become a de facto entry point for movement-primitive research, but its long-term value will depend on making inclusion criteria explicit and updating the comparison tables as new extensions appear.
  • The comparison table's time-complexity labels abstract away implementation realities such as kernel-matrix inversion versus neural-network training cost; they are useful heuristics, not engineering benchmarks.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This manuscript is a survey of trajectory-level movement primitive (MP) frameworks for robot learning from demonstration. It organizes the field into five principal families—DMPs, ProMPs, KMPs, CNMPs, and FMPs—and for each family presents the core formulation, a series of extensions, and a candid list of limitations. A comparative table, a chronological publication figure, and a large categorized set of application tables are used to support the stated goals of (i) systematic review, (ii) application taxonomy, and (iii) open-problem discussion. The abstract and introduction explicitly claim an 'encyclopedic' chronological overview covering 'over 700 papers' from nearly three decades of MP research.

Significance. If the claims of completeness and systematicity are made auditable, this would be a valuable reference for practitioners and researchers entering the MP area. The paper has real strengths: the five framework presentations are accompanied by equations and candid limitation sections; the application tables are extensive and give a broad picture of where MPs are used; and the chronological organization helps the reader see historical dependencies. The paper also provides a useful curated pointer to open-source software. The contribution is synthetic rather than technical, so its value depends entirely on the reliability and verifiability of the corpus and the consistency of the taxonomy. Those two points are where the manuscript currently needs the most work.

major comments (4)
  1. [§1.2, §7.2, §9] The abstract and §1.2 claim an 'encyclopedic' survey of 'over 700 papers' and a chronological taxonomy, but no search protocol, bibliographic databases, inclusion/exclusion criteria, screening process, or validation of the corpus is reported. This is not merely a presentation issue: the negative claim in §7.2 that 'there have been no direct improvements/extensions to the original FMP framework' is only meaningful if the corpus is exhaustive for post-2020 FMP work, and §9's exclusion of other frameworks on the ground that they are not 'appreciably distinct' is not operationalizable. As written, corpus completeness and bias cannot be audited. Please add a methodology section (e.g., PRISMA-style flow, search strings, date range, inclusion criteria, inter-rater steps) or explicitly reframe the survey as a curated, nonexhaustive narrative and weaken the 'encyclopedic' claim.
  2. [Table 2; §4.1, §4.2, §8.5] Table 2 marks the Rhythmic row for ProMPs as '-', but §4.1 states that the ProMP basis function is chosen according to whether the movement is 'discrete or rhythmic', and §4.2 and §8.5 refer to rhythmic ProMP behaviors; the original ProMP papers also evaluated rhythmic tasks in robot manipulation. This internal inconsistency undermines the 'structured and unified presentation' claim because the table does not reliably encode the taxonomy the survey itself describes. The same row for 'Multiple Demonstrations' lists only Li et al. (2023a) for DMPs, although §3.2 discusses several DMP variants trained from multiple demonstrations (e.g., Yin and Chen 2014; Pervez and Lee 2018). Please correct the table or define clearly that only base frameworks are compared.
  3. [§3.3; §8.1] The limitation in §3.3 that 'DMPs are constrained to learn from a single unique demonstration' is contradicted by extensions described earlier in the same section: §3.2 includes GMM-based DMPs (Yin and Chen 2014), task-parameterized DMPs (Pervez and Lee 2018), and probabilistic DMP representations (Calinon et al. 2012; Meier and Schaal 2016). If the limitation is meant to apply only to the original DMP formulation, the text should say so explicitly; otherwise the survey's own summaries are inconsistent about what counts as a DMP, and the comparison in Section 8 and Table 2 inherits that ambiguity.
  4. [§9] The stated criteria for excluding 'other movement primitive approaches'—differences in input demonstrations, encoded movement, or output trajectories—are reasonable in principle, but the application is not transparent. For example, 'contextual movement primitives' are dismissed in one clause, yet contextual parameterization is a recurring theme elsewhere in the survey (e.g., TP-DMPs, contextual ProMPs). Please provide a more explicit decision rule and, for each major excluded family, explain which of the three criteria led to exclusion. Without this, the boundary between Sections 3–8 and Section 9 is arbitrary and the reader cannot tell whether the omitted methods are minor variants or substantive frameworks.
minor comments (5)
  1. [§1.4] The 'Awesome Movement Primitives' repository is described as a contribution, but no URL or access date is provided. A survey that promises a continuously updated curated list should give the repository address in the text or footnote.
  2. [Figure 2] The caption does not explain whether 2025 is a partial year or how the annual counts were obtained; it also does not state whether the counts are from the authors' database or from a bibliographic source. Adding this information would improve transparency.
  3. [Table 2] The Time Complexity row uses O(n), O(log(n)), and O(n^3) without defining n. Please state the assumptions (e.g., number of training points, number of basis functions) and note that complexity depends on the inference/adaptation operation being considered.
  4. [§5.1] Equations (14)–(18) introduce the vector symbol θ without defining it; the reader is left to reconstruct the kernel feature mapping from the context. A short sentence pointing to the derivations in Huang et al. (2019b) would remove ambiguity.
  5. [Table 3] The DMP application table includes Cloud et al. (2023, 2025), which appear to be works by the current authors. The entries are legitimate application examples, but flagging them as author self-citations would improve editorial transparency.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity; the survey is a self-contained review with no derivation that reduces to its inputs.

full rationale

The paper is a survey, not a derivation. Its central claim is that it provides an encyclopedic chronological overview of movement primitive research (Abstract; Section 1.2), which rests on a literature corpus and taxonomy. No step of the survey reduces a derived quantity to an input by construction. The only apparent self-citations are Cloud et al. (2023, 2025) in the DMP applications table (Table 3), used as application entries rather than as evidence supporting the framework taxonomy; they are not load-bearing. The negative assertion in Section 7.2 that 'there have been no direct improvements/extensions to the original FMP framework' is an empirical negative claim whose auditability depends on corpus completeness; it is not a circular reduction because the survey does not use it to define or predict anything. No quoted equation or fitted parameter is repackaged as a prediction. Thus no circularity is present.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The survey makes no new technical assumptions and fits no parameters; it reproduces existing framework equations. The load-bearing assumptions are editorial: the choice of five main frameworks, the comparison axes (including the un-derived time complexities in Table 2), and the completeness of the 700+ paper corpus. No new entities are introduced.

assumptions (3)
  • domain assumption The selected set of five frameworks (DMPs, ProMPs, KMPs, CNMPs, FMPs) represents the major lineages of movement primitives, and other named variants are not 'appreciably distinct'.
    Section 9 uses this criterion to exclude other approaches (e.g., contextual MPs, interaction primitives) from the main comparison; this is an editorial judgment, not an independently measurable property, and depends on corpus completeness.
  • domain assumption The comparison axes in Table 2 (discrete/rhythmic, single/multiple demonstrations, obstacle avoidance, no parameter tuning, time complexity) are the right axes for selecting an MP framework.
    Section 8 builds the comparison on these axes without a benchmark or user-needs analysis; time complexity values are given without derivation.
  • domain assumption The equations reproduced for DMPs, ProMPs, KMPs, CNMPs, and FMPs accurately represent the original cited works.
    Sections 3-7 present these equations as descriptions of prior art; the survey does not re-derive them, and the inconsistency between Table 2 and Section 4.1 regarding rhythmic ProMPs suggests the reproduction is not fully checked.

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Cite this review

Pith. "Pith review of Movement Primitives in Robotics: A Comprehensive Survey." pith.science (2026). https://pith.science/paper/5DLJPXHJ

@misc{pith2026260102379,
  author       = {Pith},
  title        = {Pith review of: Movement Primitives in Robotics: A Comprehensive Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5DLJPXHJ}},
  note         = {Machine review of arXiv:2601.02379}
}
read the original abstract

Biological systems exhibit a continuous stream of movements, consisting of sequential segments, that allow them to perform complex tasks in a creative and versatile fashion. This observation has led researchers towards identifying elementary building blocks of motion known as movement primitives, which are well-suited for generating motor commands in autonomous systems, such as robots. In this survey, we provide an encyclopedic overview of movement primitive approaches and applications in chronological order. Concretely, we present movement primitive frameworks as a way of representing robotic control trajectories acquired through human demonstrations. Within the area of robotics, movement primitives can encode basic motions at the trajectory level, such as how a robot would grasp a cup or the sequence of motions necessary to toss a ball. Furthermore, movement primitives have been developed with the desirable analytical properties of a spring-damper system, probabilistic coupling of multiple demonstrations, using neural networks in high-dimensional systems, and more, to address difficult challenges in robotics. Although movement primitives have widespread application to a variety of fields, the goal of this survey is to inform practitioners on the use of these frameworks in the context of robotics. Specifically, we aim to (i) present a systematic review of major movement primitive frameworks and examine their strengths and weaknesses; (ii) highlight applications that have successfully made use of movement primitives; and (iii) examine open questions and discuss practical challenges when applying movement primitives in robotics.

Figures

Figures reproduced from arXiv: 2601.02379 by the authors.

Figure 1
Figure 1. An operator employing kinesthetic teaching to demonstrate a pick-and-place task to a robot manipulator. MP frameworks can be modified to account for obstacles or respond to external perturbations Rasines et al. (2024). 1.1 Motivation for Movement Primitives In this survey, we identify several traits that are used to motivate the use of MPs for various robotic applications. These ideas are distilled into the followin… view at source ↗
Figure 2
Figure 2. The distribution of MP publications over time [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. An overview of MP frameworks in terms of their similarities and differences. 8.2 Probabilistic Movement Primitives ProMPs are a probabilistic formulation of MPs that are motivated by the need to blend MPs simultaneously instead of just successive executions. ProMPs learn from several demonstrations instead of just one as seen with DMPs. From this, they learn the mean and standard deviations given the basis weight pa… view at source ↗

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SPECTRA: Context-Conditioned Spectral Movement Primitives for Robot Skill Generalization

    cs.RO 2026-07 unverdicted novelty 5.5 of 10

    Spectral Movement Primitives encode demonstrations as low-frequency Fourier coefficients and enforce joint limits by phase regulation without changing the represented end-effector path.

  2. InSight: Self-Guided Skill Acquisition via Steerable VLAs

    cs.RO 2026-06 unverdicted novelty 5.0 of 10

    InSight enables autonomous acquisition of manipulation primitives in VLAs via automated segmentation for steerability and a VLM-guided data flywheel that generates and integrates new demonstrations for tasks like pour...

Reference graph

Works this paper leans on

71 extracted references · 11 linked inside Pith · cited by 2 Pith papers

  1. [1]

    In: Proceedings of the IEEE International Conference on Robotics and Automation

    Abdelrahman AF, Mitrevski A and Pl ¨oger PG (2020) Context- aware task execution using apprenticeship learning. In: Proceedings of the IEEE International Conference on Robotics and Automation. pp. 1329–1335. Abu-Dakka F, Saveriano M and Peternel L (2024a) Learning periodic skills for robotic manipulation: Insights on orientation and impedance.Robotics and...

    2020

  2. [2]

    1398–1403

    pp. 1398–1403. Iori F, Perovic G, Cini F, Mazzeo A, Falotico E and Controzzi M (2023) Dmp-based reactive robot-to-human handover in perturbed scenarios.International Journal of Social Robotics 15(2): 233–248. Iturrate I, Kramberger A and Sloth C (2021) Quick setup of force-controlled industrial gluing tasks using learning from demonstration.Frontiers in R...

    arXiv 2023

  3. [5]

    Lauretti C, Cordella F, Guglielmelli E and Zollo L (2017) Learning by demonstration for planning activities of daily living in rehabilitation and assistive robotics.IEEE Robotics and Automation Letters2(3): 1375–1382. Lauretti C, Cordella F and Zollo L (2019) A hybrid joint/cartesian dmp-based approach for obstacle avoidance of anthropomor- phic assistive...

    2017

  4. [6]

    Robotics and Autonomous Systems135: 103690

    Pahiˇc R, Lon ˇcarevi´c Z, Gams A and Ude A (2021) Robot skill learning in latent space of a deep autoencoder neural network. Robotics and Autonomous Systems135: 103690. Pahiˇc R, Lon ˇcarevi´c Z, Ude A, Nemec B and Gams A (2018b) User feedback in latent space robotic skill learning. In: Proceedings of the IEEE-RAS International Conference on Humanoid Rob...

    2021

  5. [10]

    Shahriari E, Kramberger A, Gams A, Ude A and Haddadin S (2017) Adapting to contacts: Energy tanks and task energy for passivity-based dynamic movement primitives

    Seleem IA, El-Hussieny H and Ishii H (2023) Imitation-based motion planning and control of a multi-section continuum robot interacting with the environment.IEEE Robotics and Automation Letters8(3): 1351–1358. Shahriari E, Kramberger A, Gams A, Ude A and Haddadin S (2017) Adapting to contacts: Energy tanks and task energy for passivity-based dynamic moveme...

    2023

  6. [12]

    In:Proceedings of the IEEE- RAS International Conference on Humanoid Robots

    Prepared usingsagej.cls 102 Journal Title XX(X) Tu Z, Zhang T, Yan L and lun Lam T (2022) Whole-body control for velocity-controlled mobile collaborative robots using coupling dynamic movement primitives. In:Proceedings of the IEEE- RAS International Conference on Humanoid Robots. pp. 119–

    2022

  7. [15]

    Ijspeert AJ (2008) Central pattern generators for locomotion control in animals and robots: A review.Neural Networks21(4): 642–

    2008

  8. [17]

    Lu Z, Chen L, Dai H, Li H, Zhao Z, Zheng B, Lepora NF and Yang C (2023a) Visual-tactile robot grasping based on human skill learning from demonstrations using a wearable parallel hand exoskeleton.IEEE Robotics and Automation Letters8(9): 5384–5391. Lu Z, Si W, Wang N and Yang C (2024) Dynamic movement primitives-based human action prediction and shared co...

    2024

Show all 71 references
  1. [21]

    In:Proceedings of the IEEE International Conference on Robotics and Automation

    Prepared usingsagej.cls 90 Journal Title XX(X) Kober J and Peters J (2009) Learning motor primitives for robotics. In:Proceedings of the IEEE International Conference on Robotics and Automation. pp. 2112–2118. Kober J and Peters J (2011) Policy search for motor primitives in r...

    2009

  2. [22]

    PMLR, pp. 273–281. Daniel C, Neumann G and Peters J (2012b) Learning concurrent motor skills in versatile solution spaces. In:Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 3591–3597. Davchev T, Luck KS, Burke M, Meier F, Schaal S a...

    2022

  3. [23]

    Li C, Yang C, Ju Z and Annamalai AS (2018) An enhanced teaching interface for a robot using dmp and gmr.International Journal of Intelligent Robotics and Applications2(1): 110–121. Li G, Jin Z, V olpp M, Otto F, Lioutikov R and Neumann G (2023a) Prodmp: A unified perspective o...

    2018 arXiv

  4. [24]

    In:Proceedings of the IEEE International Conference on Robotics and Automation

    Huang R, Cheng H, Guo H, Chen Q and Lin X (2016a) Hierarchical interactive learning for a human-powered augmentation lower exoskeleton. In:Proceedings of the IEEE International Conference on Robotics and Automation. pp. 257–263. Huang R, Cheng H, Guo H, Lin X, Chen Q and Sun F...

    2018 arXiv

  5. [30]

    Li C, Lowe R and Ziemke T (2014) A novel approach to locomotion learning: Actor-critic architecture using central pattern generators and dynamic motor primitives.Frontiers in Neurorobotics8:

    2014

  6. [33]

    5058–5069

    pp. 5058–5069. Bai Y , Li F, Zhao M, Wang W, Li Y and Song R (2022) Robot manipulation skill learning based on dynamic movement primitive. In:Proceedings of the IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems. pp. 579–584. Basa...

    2022

  7. [36]

    12126– 12134

    pp. 12126– 12134. Sauser EL, Argall BD, Metta G and Billard AG (2012) Iterative learning of grasp adaptation through human corrections. Robotics and Autonomous Systems60(1): 55–71. Saveriano M, Abu-Dakka FJ, Kramberger A and Peternel L (2023) Dynamic movement primitives in rob...

    2012

  8. [40]

    In:Proceedings of the IEEE International Conference on Robotics and Automation

    Ning K, Kulvicius T, Tamosiunaite M and W ¨org¨otter F (2011) Accurate position and velocity control for trajectories based on dynamic movement primitives. In:Proceedings of the IEEE International Conference on Robotics and Automation. pp. 5006–5011. Ning K, Kulvicius T, Tamos...

    2011

  9. [42]

    pp. 925–940. Nakanishi J, Morimoto J, Endo G, Cheng G, Schaal S and Kawato M (2004b) Learning from demonstration and adaptation of Prepared usingsagej.cls Gutierrez and Beksi 95 biped locomotion.Robotics and Autonomous Systems47(2-3): 79–91. Nakanishi J, Rawlik K and Vijayakum...

    2011

  10. [43]

    In:Proceedigns of the International Symposium on Robotics Research

    Haddadin S, Weitschat R, Huber F, ¨Ozparpucu MC, Mansfeld N and Albu-Sch ¨affer A (2016) Optimal control for viscoelastic robots and its generalization in real-time. In:Proceedigns of the International Symposium on Robotics Research. Springer, pp. 131–148. Han L, Kang P, Chen ...

    2016

  11. [44]

    In:Proceedings of the RSI Prepared usingsagej.cls 96 Journal Title XX(X) International Conference on Robotics and Mechatronics

    Noohian A, Raisi M and Khodaygan S (2022) A framework for learning dynamic movement primitives with deep reinforcement learning. In:Proceedings of the RSI Prepared usingsagej.cls 96 Journal Title XX(X) International Conference on Robotics and Mechatronics. IEEE, pp. 329–334. N...

    2022

  12. [45]

    Dimeas F and Doulgeri Z (2019) Progressive automation of repetitive tasks involving both translation and rotation

    DeWolf T, Stewart TC, Slotine JJ and Eliasmith C (2016) A spiking neural model of adaptive arm control.Proceedings of the Royal Society B: Biological Sciences283(1843): 20162134. Dimeas F and Doulgeri Z (2019) Progressive automation of repetitive tasks involving both translati...

    2016

  13. [46]

    In:Proceedings of the IEEE-RAS International Conference on Humanoid Robots

    Deniˇsa M, Petri ˇc T, Asfour T and Ude A (2013) Synthesizing compliant reaching movements by searching a database of example trajectories. In:Proceedings of the IEEE-RAS International Conference on Humanoid Robots. pp. 540–546. Deniˇsa M, Petri ˇc T, Gams A and Ude A (2016) A...

    2013

  14. [47]

    In: Proceedings of the IEEE International Conference on Robotics and Automation

    De Andres MO, Ardakani MMG and Robertsson A (2018) Reinforcement learning for 4-finger-gripper manipulation. In: Proceedings of the IEEE International Conference on Robotics and Automation. pp. 4257–4262. Degallier S, Righetti L, Gay S and Ijspeert A (2011) Toward simple contr...

    2018

  15. [49]

    Cai C and Liu S (2023) A probabilistic dynamic movement primitives framework on human hand motion prediction for an object transfer scenario.IFAC-PapersOnLine56(2): 8327–

    Caccavale R, Saveriano M, Finzi A and Lee D (2019) Kinesthetic teaching and attentional supervision of structured tasks in human–robot interaction.Autonomous Robots43: 1291–1307. Cai C and Liu S (2023) A probabilistic dynamic movement primitives framework on human hand motion ...

    2019

  16. [50]

    In:Proceedings of the IEEE-RAS International Conference on Humanoid Robots

    Rai A, Meier F, Ijspeert A and Schaal S (2014) Learning coupling terms for obstacle avoidance. In:Proceedings of the IEEE-RAS International Conference on Humanoid Robots. pp. 512–518. Rai A, Sutanto G, Schaal S and Meier F (2017) Learning feedback terms for reactive planning a...

    2014

  17. [53]

    In:Proceedings of the IEEE International Conference on Robotics and Automation

    Dragan AD, Muelling K, Bagnell JA and Srinivasa SS (2015) Movement primitives via optimization. In:Proceedings of the IEEE International Conference on Robotics and Automation. pp. 2339–2346. Duan A, Camoriano R, Ferigo D, Calandriello D, Rosasco L and Pucci D (2018) Constraine...

    2015

  18. [54]

    In:Proceedings of the IEEE International Conference on Robotics and Automation

    R¨ueckert E, Mundo J, Paraschos A, Peters J and Neumann G (2015) Extracting low-dimensional control variables for movement primitives. In:Proceedings of the IEEE International Conference on Robotics and Automation. pp. 1511–1518. Samant R, Behera L and Pandey G (2016) Adaptive...

    2015

  19. [59]

    In: Proceedings of the European Control Conference

    Sidiropoulos A, Karayiannidis Y and Doulgeri Z (2019) Human- robot collaborative object transfer using human motion prediction based on dynamic movement primitives. In: Proceedings of the European Control Conference. IEEE, pp. 2583–2588. Sidiropoulos A, Karayiannidis Y and Dou...

    2019

  20. [60]

    In:Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems

    Silv´erio J, Huang Y , Abu-Dakka FJ, Rozo L and Caldwell DG (2019) Uncertainty-aware imitation learning using kernelized movement primitives. In:Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 90–97. Silv´erio J, Rozo L, Calinon S an...

    2019

  21. [61]

    In:Proceeding of the IEEE International Conference on Robotics and Automation

    Singh S, Xu T and Chang Q (2025) Collaborative motion planning for multi-manipulator systems through reinforcement learning and dynamic movement primitives. In:Proceeding of the IEEE International Conference on Robotics and Automation. pp. 3369–3375. Sloth C, Kramberger A and ...

    2025 arXiv

  22. [64]

    Luo RC and Mai L (2021) Dual-filtering for on-line simultaneously estimate weights and phase parameter of probabilistic movement primitives for human-robot collaboration

    Luo Q, Wu J and Gombolay M (2020) A generalized robotic handwriting learning system based on dynamic movement primitives (dmps).arXiv preprint arXiv:2012.03898. Luo RC and Mai L (2021) Dual-filtering for on-line simultaneously estimate weights and phase parameter of probabilis...

    2020 arXiv

  23. [77]

    In: Proceedings of the IEEE-RAS International Conference on Humanoid Robots

    V orndamme J, Carvalho J, Laha R, Koert D, Figueredo L, Peters J and Haddadin S (2022) Integrated bi-manual motion generation and control shaped for probabilistic movement primitives. In: Proceedings of the IEEE-RAS International Conference on Humanoid Robots. pp. 202–209. Vug...

    2022

  24. [97]

    pp. 12–20. Awesome Movement Primitives (2025)https: //github.com/robotic-vision-lab/ Awesome-Movement-Primitives. Bahl S, Mukadam M, Gupta A and Pathak D (2020) Neural dynamic policies for end-to-end sensorimotor learning. In: Proceedings of the Advances in Neural Information ...

    2025

  25. [98]

    In:Proceedings of the IEEE International Conference on Robotics and Automation

    Prepared usingsagej.cls Gutierrez and Beksi 97 Parlaktuna M, Tunaoglu D, Sahin E and Ugur E (2012) Closed- loop primitives: A method to generate and recognize reaching actions from demonstration. In:Proceedings of the IEEE International Conference on Robotics and Automation. p...

    2012 arXiv

  26. [103]

    Zhou J, Peng H, Zheng M, Wei Z, Fan T and Song R (2024a) Tra- jectory deformation-based multi-modal adaptive compliance control for a wearable lower limb rehabilitation robot.IEEE Transactions on Neural Systems and Rehabilitation Engineer- ing. Zhou J, Sun Y , Song R and Wei Z...

    2022

  27. [126]

    Ude A, Nemec B, Petri ´c T and Morimoto J (2014) Orientation in cartesian space dynamic movement primitives

    Ude A, Gams A, Asfour T and Morimoto J (2010) Task-specific generalization of discrete and periodic dynamic movement primitives.IEEE Transactions on Robotics26(5): 800–815. Ude A, Nemec B, Petri ´c T and Morimoto J (2014) Orientation in cartesian space dynamic movement primiti...

    2010

  28. [135]

    In:Proceedings of the IEEE-RAS International Conference on Humanoid Robots

    Stulp F, Oztop E, Pastor P, Beetz M and Schaal S (2009b) Compact models of motor primitive variations for predictable reaching and obstacle avoidance. In:Proceedings of the IEEE-RAS International Conference on Humanoid Robots. pp. 589–595. Stulp F, Raiola G, Hoarau A, Ivaldi S...

    2013

  29. [137]

    Dermy O, Paraschos A, Ewerton M, Peters J, Charpillet F and Ivaldi S (2017) Prediction of intention during interaction with icub with probabilistic movement primitives.Frontiers in Robotics and AI4:

    2017

  30. [138]

    R¨uckert EA, Neumann G, Toussaint M and Maass W (2013) Learned graphical models for probabilistic planning provide a Prepared usingsagej.cls Gutierrez and Beksi 99 new class of movement primitives.Frontiers in Computational Neuroscience6:

    2013

  31. [166]

    Lazaric A and Ghavamzadeh M (2010) Bayesian multi-task reinforcement learning

    Lauretti C, Tamantini C and Zollo L (2024) A new dmp scaling method for robot learning by demonstration and application to the agricultural domain.IEEE Access12: 7661–7673. Lazaric A and Ghavamzadeh M (2010) Bayesian multi-task reinforcement learning. In:Proceedings of the Int...

    2024

  32. [279]

    In:Proceedings of the IEEE-RAS International Conference on Humanoid Robots

    M¨ulling K, Kober J and Peters J (2010) Learning table tennis with a mixture of motor primitives. In:Proceedings of the IEEE-RAS International Conference on Humanoid Robots. pp. 411–416. Nah MC, Krotov A, Russo M, Sternad D and Hogan N (2020) Dynamic primitives facilitate mani...

    2010

  33. [330]

    In:Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems

    Reinhart RF and Steil JJ (2014) Efficient policy search with a parameterized skill memory. In:Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 1400–1407. Reinhart RF and Steil JJ (2015) Efficient policy search in low-dimensional embed...

    2014 arXiv

  34. [397]

    Angelov D, Hristov Y , Burke M and Ramamoorthy S (2020) Composing diverse policies for temporally extended tasks

    Andr´e J, Teixeira C, Santos CP and Costa L (2015) Adapting biped locomotion to sloped environments: Combining reinforcement learning with dynamical systems.Journal of Intelligent & Robotic Systems80: 625–640. Angelov D, Hristov Y , Burke M and Ramamoorthy S (2020) Composing d...

    2015

  35. [412]

    Petriˇc T, Gams A, Colasanto L, Ijspeert AJ and Ude A (2018) Accelerated sensorimotor learning of compliant movement primitives.IEEE Transactions on Robotics34(6): 1636–1642. Petriˇc T, Gams A, Ijspeert AJ and ˇZlajpah L (2011) On-line frequency adaptation and movement imitati...

    2018

  36. [440]

    In:Proceedings of the IEEE-RAS International Conference on Humanoid Robots

    Chen N, Karl M and Van Der Smagt P (2016) Dynamic movement primitives in latent space of time-dependent variational autoencoders. In:Proceedings of the IEEE-RAS International Conference on Humanoid Robots. pp. 629–636. Chen N and Pan YJ (2024) Vision-based dexterous motion pla...

    2016

  37. [477]

    Gribovskaya E, Khansari-Zadeh SM and Billard A (2011) Learning non-linear multivariate dynamics of motion in robotic manipulators.The International Journal of Robotics Research 30(1): 80–117. Guan H, Wang B, Gong J and Chen H (2023) Coordinated motion planning for heterogeneou...

    2011

  38. [494]

    Peters J, Kober J, M ¨ulling K, Kr ¨amer O and Neumann G (2013) Towards robot skill learning: From simple skills to table tennis

    Peternel L, Tsagarakis N, Caldwell D and Ajoudani A (2018b) Robot adaptation to human physical fatigue in human–robot co-manipulation.Autonomous Robots42: 1011–1021. Peters J, Kober J, M ¨ulling K, Kr ¨amer O and Neumann G (2013) Towards robot skill learning: From simple skill...

    2013

  39. [653]

    Ijspeert AJ, Nakanishi J and Schaal S (2001) Trajectory formation for imitation with nonlinear dynamical systems

    Ijspeert AJ, Nakanishi J, Hoffmann H, Pastor P and Schaal S (2013) Dynamical movement primitives: Learning attractor models for motor behaviors.Neural Computation25(2): 328–373. Ijspeert AJ, Nakanishi J and Schaal S (2001) Trajectory formation for imitation with nonlinear dyna...

    2013

  40. [764]

    Zhang Y , Gong D and Yu J (2023b) Human-like motion of robotic arm based on dynamic movement primitives and deep neural network

    Zhang X, Wang Y , Li C, Fahmy A and Sienz J (2024a) Innovative multi-dimensional learning algorithm and experiment design for human-robot cooperation.Applied Mathematical Modelling127: 730–751. Zhang Y , Gong D and Yu J (2023b) Human-like motion of robotic arm based on dynamic...

    2025

  41. [830]

    Gams A, Nemec B, Petri ˇc T and Ude A (2014b) Coupling of trajectories for human–robot cooperative tasks.Advances in Robot Kinematics: 537–544. Gams A, Nemec B, Zlajpah L, W ¨achter M, Ijspeert A, Asfour T and Ude A (2013) Modulation of motor primitives using force feedback: I...

    2013

  42. [872]

    IEEE Transactions on Automation Science and Engineering 22: 4525–4536

    Hao X, Li Z, Huang P, Shi P and Li G (2025) Hierarchical task- oriented whole-body locomotion of a walking exoskeleton using adaptive dynamic motion primitive for cart pushing. IEEE Transactions on Automation Science and Engineering 22: 4525–4536. Hazara M and Kyrki V (2017) M...

    2025 arXiv

  43. [1013]

    In:Proceedings of the International Conference on Robotics in Alpe-Adria Danube Region

    Lonˇcarevi´c Z, Petri ˇc T and Gams A (2022a) Fitting constrained trajectory with high variability into redundant robot workspace. In:Proceedings of the International Conference on Robotics in Alpe-Adria Danube Region. Springer, pp. 167–175. Lonˇcarevi´c Z, Simoniˇc M, Ude A a...

    2020

  44. [1019]

    Luo J, Liu W, Qi W, Hu J, Chen J and Yang C (2023) A vision-based virtual fixture with robot learning for teleoperation.Robotics and Autonomous Systems164: 104414. Luo L, Foo MJ, Ramanathan M, Er JK, Chiam CH, Li L, Yau WY and Ang WT (2022) Trajectory generation and control of...

    2023

  45. [1062]

    Zhang H, Fu M, Luo H and Zhou W (2017) Robust human action recognition using dynamic movement features

    Zhai DH, Xia Z, Wu H and Xia Y (2022) A motion planning method for robots based on dmps and modified obstacle- avoiding algorithm.IEEE Transactions on Automation Science and Engineering20(4): 2678–2688. Zhang H, Fu M, Luo H and Zhou W (2017) Robust human action recognition usi...

    2022

  46. [1134]

    In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems

    Zhou Y , Gao J and Asfour T (2019) Learning via-point movement primitives with inter-and extrapolation capabilities. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 4301–4308. Zhou Y , Gao J and Asfour T (2020) Movement primitive...

    2019

  47. [1184]

    In:Proceedings of the IEEE International Conference on Robotics and Automation

    Saveriano M, Franzel F and Lee D (2019) Merging position and orientation motion primitives. In:Proceedings of the IEEE International Conference on Robotics and Automation. pp. 7041–7047. Schaal S (1999) Is imitation learning the route to humanoid robots? Trends in Cognitive Sc...

    2019

  48. [1370]

    In: Proceedings of the IEEE International Conference on Robotics and Automation

    Su H, Hu Y , Li Z, Knoll A, Ferrigno G and De Momi E (2020) Reinforcement learning based manipulation skill transferring for robot-assisted minimally invasive surgery. In: Proceedings of the IEEE International Conference on Robotics and Automation. pp. 2203–2208. Su H, Mariani...

    2020 arXiv

  49. [1515]

    In:Proceedings of the IEEE International Conference on Control & Automation

    Lu Z, Wang N, Li M and Yang C (2022a) A novel dynamic movement primitives-based skill learning and transfer framework for multi-tool use. In:Proceedings of the IEEE International Conference on Control & Automation. pp. 1–8. Lu Z, Wang N, Li Q and Yang C (2023b) A trajectory an...

    2025

  50. [1535]

    In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems

    Chi W, Liu J, Abdelaziz ME, Dagnino G, Riga C, Bicknell C and Yang GZ (2018) Trajectory optimization of robot-assisted endovascular catheterization with reinforcement learning. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 3875...

    2018

  51. [1594]

    In: Proceedings of the Conference on Robot Learning

    Maeda G, Ewerton M, Osa T, Busch B and Peters J (2017b) Active incremental learning of robot movement primitives. In: Proceedings of the Conference on Robot Learning. PMLR, pp. 37–46. Maeda G, Neumann G, Ewerton M, Lioutikov R and Peters J (2018) A probabilistic framework for ...

    2018 arXiv

  52. [2010]

    Springer, pp. 211–223. Kroemer OB, Detry R, Piater J and Peters J (2010) Combining active learning and reactive control for robot grasping.Robotics and Autonomous systems58(9): 1105–1116. Krug R and Dimitrov D (2015) Model predictive motion control based on generalized dynamic...

    2010

  53. [2084]

    Schaal S, Kotosaka S and Sternad D (2000) Nonlinear dynamical systems as movement primitives

    Schaal S, Atkeson CG and Vijayakumar S (2002) Scalable techniques from nonparametric statistics for real time robot learning.Applied Intelligence17(1): 49–60. Schaal S, Kotosaka S and Sternad D (2000) Nonlinear dynamical systems as movement primitives. In:Proceedings of the IE...

    2002 arXiv

  54. [2624]

    Paraschos A, Neumann G and Peters J (2013b) A probabilistic approach to robot trajectory generation

    Paraschos A, Lioutikov R, Peters J and Neumann G (2017) Probabilistic prioritization of movement primitives.IEEE Robotics and Automation Letters2(4): 2294–2301. Paraschos A, Neumann G and Peters J (2013b) A probabilistic approach to robot trajectory generation. In:Proceedings ...

    2017

  55. [2862]

    Autonomous Robots28: 21–44

    Beetz M, Stulp F, Esden-Tempski P, Fedrizzi A, Klank U, Kresse I, Maldonado A and Ruiz F (2010) Generality and legibility in mobile manipulation: Learning skills for routine tasks. Autonomous Robots28: 21–44. Beik-Mohammadi H, Kerzel M, Pleintinger B, Hulin T, Reisich P, Schmi...

    2010

  56. [3282]

    Robotics and Autonomous Systems91: 270–283

    Mukovskiy A, Vassallo C, Naveau M, Stasse O, Sou `eres P and Giese MA (2017) Adaptive synthesis of dynamically feasible full-body movements for the humanoid robot hrp-2 by flexible combination of learned dynamic movement primitives. Robotics and Autonomous Systems91: 270–283. ...

    2017

  57. [3796]

    In: Proceedings of the Mediterranean Conference on Control and Automation

    Kastritsi T, Sidiropoulos A and Doulgeri Z (2018b) A phri framework for modifying a robot’s kinematic behaviour via varying stiffness and dynamical system synchronization. In: Proceedings of the Mediterranean Conference on Control and Automation. IEEE, pp. 1–6. Katyara S, Ficu...

  58. [3860]

    Yin X and Chen Q (2014) Learning nonlinear dynamical system for movement primitives

    Yao X, Chen Y and Tripp B (2023) Improved generalization of probabilistic movement primitives for manipulation trajecto- ries.IEEE Robotics and Automation Letters9(1): 287–294. Yin X and Chen Q (2014) Learning nonlinear dynamical system for movement primitives. In:Proceedings ...

    2023

  59. [4290]

    me and you together

    Ewerton M, Maeda G, Peters J and Neumann G (2015a) Learning motor skills from partially observed movements executed at different speeds. In:Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 456–463. Ewerton M, Neumann G, Lioutikov R, A...

    2014

  60. [5345]

    In:Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems

    Schroecker Y , Ben Amor H and Thomaz A (2016) Directing policy search with interactively taught via-points. In:Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems. pp. 1052–1059. Schwaner KL, Dall’Alba D, Jensen PT, Fiorini P and Savarimu...

    2016

  61. [6215]

    In:Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems

    Aein MJ, Aksoy EE, Tamosiunaite M, Papon J, Ude A and W¨org¨otter F (2013) Toward a library of manipulation actions based on semantic object-action relations. In:Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 4555–4562. Agostini A, ...

    2013

  62. [7799]

    Huang H, Zeng C, Cheng L and Yang C (2023) Toward generalizable robotic dual-arm flipping manipulation.IEEE Transactions on Industrial Electronics71(5): 4954–4962. Huang L, Zheng J, Gao Y , Song Q and Liu Y (2025) A lower limb exoskeleton adaptive control method based on model...

    2023

  63. [8332]

    Calinon S (2018) Learning from demonstration (programming by demonstration).Encyclopedia of Robotics: 1–8

    Calinon S (2016) A tutorial on task-parameterized movement learning and retrieval.Intelligent Service Robotics9: 1–29. Calinon S (2018) Learning from demonstration (programming by demonstration).Encyclopedia of Robotics: 1–8. Calinon S, D’halluin F, Sauser EL, Caldwell DG and ...

    2016

Pith tools

Reviewed August 3, 2026 · model on record in the stance chip above.