Using probabilistic movement primitives in robotics
Movement Primitives are a well-established paradigm for modular movement representation and generation. They provide a data-driven representation of movements and support generalization to novel situations, temporal modulation, sequencing of primitives and controllers for executing the primitive on...
Saved in:
| Published in: | Autonomous robots Vol. 42; no. 3; pp. 529 - 551 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Springer US
01.03.2018
Springer Nature B.V |
| Subjects: | |
| ISSN: | 0929-5593, 1573-7527 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Movement Primitives are a well-established paradigm for modular movement representation and generation. They provide a data-driven representation of movements and support generalization to novel situations, temporal modulation, sequencing of primitives and controllers for executing the primitive on physical systems. However, while many MP frameworks exhibit some of these properties, there is a need for a unified framework that implements all of them in a principled way. In this paper, we show that this goal can be achieved by using a probabilistic representation. Our approach models trajectory distributions learned from stochastic movements. Probabilistic operations, such as conditioning can be used to achieve generalization to novel situations or to combine and blend movements in a principled way. We derive a stochastic feedback controller that reproduces the encoded variability of the movement and the coupling of the degrees of freedom of the robot. We evaluate and compare our approach on several simulated and real robot scenarios. |
|---|---|
| AbstractList | Movement Primitives are a well-established paradigm for modular movement representation and generation. They provide a data-driven representation of movements and support generalization to novel situations, temporal modulation, sequencing of primitives and controllers for executing the primitive on physical systems. However, while many MP frameworks exhibit some of these properties, there is a need for a unified framework that implements all of them in a principled way. In this paper, we show that this goal can be achieved by using a probabilistic representation. Our approach models trajectory distributions learned from stochastic movements. Probabilistic operations, such as conditioning can be used to achieve generalization to novel situations or to combine and blend movements in a principled way. We derive a stochastic feedback controller that reproduces the encoded variability of the movement and the coupling of the degrees of freedom of the robot. We evaluate and compare our approach on several simulated and real robot scenarios. |
| Author | Neumann, Gerhard Paraschos, Alexandros Daniel, Christian Peters, Jan |
| Author_xml | – sequence: 1 givenname: Alexandros surname: Paraschos fullname: Paraschos, Alexandros email: Paraschos@ias.tu-darmstadt.de organization: Technische Universität Darmstadt – sequence: 2 givenname: Christian surname: Daniel fullname: Daniel, Christian organization: Bosch Center for Artificial Intelligence – sequence: 3 givenname: Jan surname: Peters fullname: Peters, Jan organization: Technische Universität Darmstadt, Max-Planck-Institut für Intelligente Systeme – sequence: 4 givenname: Gerhard surname: Neumann fullname: Neumann, Gerhard organization: Computational Learning for Autonomous Systems, School of Computer Science, University of Lincoln |
| BookMark | eNp9kEtLAzEUhYNUsK3-AHcDrqM372YpxRcU3Nh1yGQyJaXNaJIW_PemjCAIurpw7_nuOZwZmsQheoSuCdwSAHWXCQjCMRCFteQLrM7QlAjFsBJUTdAUNNVYCM0u0CznLQBoBTBFbJ1D3DTvaWhtG3Yhl-Ca_XD0ex9LXYd9KOHocxNiUzVDPedLdN7bXfZX33OO1o8Pb8tnvHp9elner7BjRBbMaUtVZwXrhKeEulZ6Ak60nKteMdd7zYERrhQXQvJWdhy0kx241llLhGRzdDP-rek-Dj4Xsx0OKVZLQ6nQQIEuaFWRUeXSkHPyvTmltunTEDCnbszYjandmFM3RlVG_WJcKLaEIZZkw-5fko5kri5x49NPpr-hLz8yeZQ |
| CitedBy_id | crossref_primary_10_1109_LRA_2021_3056367 crossref_primary_10_1007_s43154_022_00082_9 crossref_primary_10_3390_robotics11060126 crossref_primary_10_1109_TIE_2024_3384602 crossref_primary_10_1016_j_engappai_2024_108310 crossref_primary_10_1109_TRO_2025_3593109 crossref_primary_10_1007_s10514_021_10030_9 crossref_primary_10_1007_s10846_022_01605_4 crossref_primary_10_1016_j_robot_2021_103842 crossref_primary_10_3389_fnins_2022_987472 crossref_primary_10_3389_frobt_2021_721890 crossref_primary_10_1109_TIE_2025_3539329 crossref_primary_10_1109_TRO_2024_3390052 crossref_primary_10_1109_LRA_2023_3248443 crossref_primary_10_3390_robotics13070107 crossref_primary_10_1177_0278364919868279 crossref_primary_10_1016_j_neucom_2024_128036 crossref_primary_10_1109_TRO_2024_3381558 crossref_primary_10_1109_TASE_2025_3559696 crossref_primary_10_1177_02783649231193046 crossref_primary_10_1016_j_ifacol_2021_11_186 crossref_primary_10_1080_00423114_2021_1930070 crossref_primary_10_1016_j_cie_2023_109345 crossref_primary_10_1109_LRA_2022_3184003 crossref_primary_10_1109_LRA_2020_3005892 crossref_primary_10_1109_TCDS_2021_3137262 crossref_primary_10_1016_j_ridd_2021_103854 crossref_primary_10_1007_s10845_020_01686_8 crossref_primary_10_1016_j_neucom_2023_126781 crossref_primary_10_1109_TASE_2022_3217468 crossref_primary_10_1109_TASE_2024_3403833 crossref_primary_10_1109_LRA_2021_3068891 crossref_primary_10_1109_LRA_2021_3068892 crossref_primary_10_1109_LRA_2024_3349809 crossref_primary_10_3389_fnbot_2022_1086578 crossref_primary_10_3390_s20195505 crossref_primary_10_1109_LRA_2022_3146614 crossref_primary_10_1002_rob_22230 crossref_primary_10_1109_LRA_2021_3060414 crossref_primary_10_3389_fnbot_2023_1320251 crossref_primary_10_1109_LRA_2019_2928760 crossref_primary_10_3389_frobt_2021_638849 crossref_primary_10_1177_0278364919846363 crossref_primary_10_1109_LRA_2025_3577430 crossref_primary_10_1109_TRO_2025_3582829 crossref_primary_10_1007_s10846_022_01696_z crossref_primary_10_1007_s10514_022_10067_4 crossref_primary_10_1007_s10846_024_02051_0 crossref_primary_10_3390_s24123964 crossref_primary_10_1109_TSMC_2023_3285588 crossref_primary_10_3390_s22082862 crossref_primary_10_1109_LRA_2020_2970620 crossref_primary_10_1146_annurev_control_061623_094742 crossref_primary_10_1109_LRA_2024_3385691 crossref_primary_10_1109_TASE_2025_3592739 crossref_primary_10_1109_TASE_2021_3127574 crossref_primary_10_1109_LRA_2021_3086666 crossref_primary_10_3390_biomimetics9120738 crossref_primary_10_1109_TNNLS_2024_3397356 crossref_primary_10_3389_frobt_2022_772228 crossref_primary_10_1631_FITEE_2200065 crossref_primary_10_3389_frobt_2019_00089 crossref_primary_10_1109_TCDS_2023_3296166 crossref_primary_10_1109_TMECH_2022_3196036 crossref_primary_10_3390_app15063171 crossref_primary_10_1007_s12652_023_04551_7 crossref_primary_10_1109_ACCESS_2018_2873718 crossref_primary_10_1109_THMS_2021_3107523 crossref_primary_10_1016_j_inffus_2024_102379 crossref_primary_10_3389_fnbot_2019_00056 crossref_primary_10_3390_electronics12194122 crossref_primary_10_1016_j_neucom_2024_127711 crossref_primary_10_1177_02783649221143399 crossref_primary_10_1109_LRA_2021_3125058 crossref_primary_10_1109_TRO_2021_3127108 crossref_primary_10_3390_s21248389 crossref_primary_10_1108_IR_12_2023_0322 crossref_primary_10_1109_LRA_2025_3546109 crossref_primary_10_1016_j_automatica_2023_111120 crossref_primary_10_1007_s11633_025_1560_6 crossref_primary_10_1109_TIE_2023_3250746 crossref_primary_10_1016_j_robot_2022_104312 crossref_primary_10_1109_ACCESS_2024_3422808 crossref_primary_10_3389_fnins_2021_694914 crossref_primary_10_1109_LRA_2024_3477169 crossref_primary_10_1016_j_robot_2024_104869 crossref_primary_10_1109_TASE_2022_3233851 crossref_primary_10_1016_j_cie_2024_110144 crossref_primary_10_1109_TASE_2024_3469961 crossref_primary_10_1007_s11432_024_4322_6 crossref_primary_10_1109_LRA_2020_2976314 crossref_primary_10_3389_frobt_2023_1256763 crossref_primary_10_1109_LRA_2023_3333741 crossref_primary_10_1016_j_robot_2025_105056 crossref_primary_10_1109_LRA_2021_3061310 crossref_primary_10_1109_LRA_2023_3279618 crossref_primary_10_1109_TRO_2023_3286074 crossref_primary_10_1109_TRO_2023_3303011 |
| Cites_doi | 10.1007/s11370-015-0187-9 10.1109/HUMANOIDS.2013.7030017 10.1177/0278364912472380 10.1109/ROBOT.2006.1641933 10.1177/1059712311419378 10.1016/j.neunet.2008.03.014 10.1109/IROS.2010.5649089 10.1162/NECO_a_00393 10.1145/1553374.1553508 10.1109/IROS.2010.5648931 10.1109/HUMANOIDS.2012.6651499 10.1109/TRO.2011.2159412 10.1109/MRA.2010.936947 10.1016/j.neunet.2011.02.004 10.1007/978-3-319-22873-0_17 10.1177/0278364911402527 10.1109/TRO.2010.2065430 10.1109/IROS.2011.6095059 10.1007/s10514-007-9051-x 10.1177/0278364911428653 10.1109/IROS.2015.7353412 10.1007/11008941_60 10.1109/IROS.2012.6386047 10.1109/ROBOT.2010.5509672 10.1016/j.robot.2004.03.003 10.1109/HUMANOIDS.2014.7041413 10.1038/nn963 10.1073/pnas.0500199102 10.1109/TRO.2011.2163863 10.1609/aaai.v27i1.8543 10.1109/ROBOT.2009.5152385 10.1109/ICRA.2015.7139390 10.1145/1553374.1553471 10.1016/j.robot.2012.05.004 10.1016/0024-3795(88)90223-6 10.1109/TRO.2014.2304775 10.1007/s10514-011-9235-2 10.1126/science.1210617 |
| ContentType | Journal Article |
| Copyright | Springer Science+Business Media, LLC 2017 Autonomous Robots is a copyright of Springer, (2017). All Rights Reserved. |
| Copyright_xml | – notice: Springer Science+Business Media, LLC 2017 – notice: Autonomous Robots is a copyright of Springer, (2017). All Rights Reserved. |
| DBID | AAYXX CITATION 7SC 7SP 7TB 8FD 8FE 8FG ABJCF AFKRA ARAPS BENPR BGLVJ CCPQU DWQXO F28 FR3 HCIFZ JQ2 L6V L7M L~C L~D M7S P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS S0W |
| DOI | 10.1007/s10514-017-9648-7 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Technology Collection ProQuest One ProQuest Central Korea ANTE: Abstracts in New Technology & Engineering Engineering Research Database SciTech Premium Collection ProQuest Computer Science Collection ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection DELNET Engineering & Technology Collection |
| DatabaseTitle | CrossRef Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Advanced Technologies & Aerospace Collection ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace Engineering Collection ANTE: Abstracts in New Technology & Engineering Advanced Technologies & Aerospace Collection Engineering Database ProQuest One Academic Eastern Edition Electronics & Communications Abstracts ProQuest Technology Collection ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest DELNET Engineering and Technology Collection Materials Science & Engineering Collection Engineering Research Database ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | Technology Collection |
| Database_xml | – sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1573-7527 |
| EndPage | 551 |
| ExternalDocumentID | 10_1007_s10514_017_9648_7 |
| GrantInformation_xml | – fundername: Seventh Framework Programme (BE) grantid: 248273 funderid: http://dx.doi.org/10.13039/501100004963 – fundername: Seventh Framework Programme grantid: 600716; 270327 funderid: http://dx.doi.org/10.13039/501100004963 |
| GroupedDBID | -59 -5G -BR -EM -Y2 -~C -~X .86 .DC .VR 06D 0R~ 0VY 199 1N0 1SB 2.D 203 23N 28- 29~ 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6NX 6TJ 78A 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJCF ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCEE ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP EBLON EBS EIOEI EJD ESBYG F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW L6V LAK LLZTM M4Y M7S MA- N2Q NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P62 P9O PF0 PT4 PT5 PTHSS Q2X QOK QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZC RZE RZK S0W S16 S1Z S26 S27 S28 S3B SAP SCLPG SCO SCV SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TEORI TSG TSK TSV TUC U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z7R Z7S Z7X Z7Z Z83 Z86 Z88 Z8M Z8N Z8T Z8W Z92 ZMTXR _50 ~02 ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB 7SC 7SP 7TB 8FD DWQXO F28 FR3 JQ2 L7M L~C L~D PKEHL PQEST PQQKQ PQUKI PRINS |
| ID | FETCH-LOGICAL-c316t-42b27da53d5e212cb6e10c5b447f73cfe9403147745564b6d409c6d0cbcaa1563 |
| IEDL.DBID | M7S |
| ISICitedReferencesCount | 159 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000425113800002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0929-5593 |
| IngestDate | Thu Nov 06 14:26:37 EST 2025 Tue Nov 18 21:10:27 EST 2025 Sat Nov 29 02:41:59 EST 2025 Fri Feb 21 02:33:44 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Keywords | Control Movement primitives Trajectory representation Imitation learning Robotics |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c316t-42b27da53d5e212cb6e10c5b447f73cfe9403147745564b6d409c6d0cbcaa1563 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2259020282 |
| PQPubID | 326361 |
| PageCount | 23 |
| ParticipantIDs | proquest_journals_2259020282 crossref_primary_10_1007_s10514_017_9648_7 crossref_citationtrail_10_1007_s10514_017_9648_7 springer_journals_10_1007_s10514_017_9648_7 |
| PublicationCentury | 2000 |
| PublicationDate | 20180300 2018-3-00 20180301 |
| PublicationDateYYYYMMDD | 2018-03-01 |
| PublicationDate_xml | – month: 3 year: 2018 text: 20180300 |
| PublicationDecade | 2010 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York – name: Dordrecht |
| PublicationTitle | Autonomous robots |
| PublicationTitleAbbrev | Auton Robot |
| PublicationYear | 2018 |
| Publisher | Springer US Springer Nature B.V |
| Publisher_xml | – name: Springer US – name: Springer Nature B.V |
| References | Rueckert, E., Mundo, J., Paraschos, A., Peters, J., & Neumann, G. (2015). Extracting low-dimensional control variables for movement primitives. In International conference on robotics and automation (ICRA) (pp. 1511–1518). MüllingKKoberJKroemerOPetersJLearning to select and generalize striking movements in robot table tennisThe International Journal of Robotics Research201332326327910.1177/0278364912472380 Neumann, G., Maass, W., & Peters, J. (2009). Learning complex motions by sequencing simpler motion templates. In International conference on machine learning (ICML) (pp. 753–760) Calinon, S., Sardellitti, I., & Caldwell, D. G. (2010b). Learning-based control strategy for safe human–robot interaction exploiting task and robot redundancies. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 249–254). ForteDGamsAMorimotoJUdeAOn-line motion synthesis and adaptation using a trajectory databaseRobotics and Autonomous Systems2012601327133910.1016/j.robot.2012.05.004 HighamNJComputing a nearest symmetric positive semidefinite matrixLinear Algebra and its Applications198810310311894399710.1016/0024-3795(88)90223-60649.65026 CalinonSA tutorial on task-parameterized movement learning and retrievalIntelligent Service Robotics20169112910.1007/s11370-015-0187-9 Paraschos, A., Neumann, G., & Peters, J. (2013b). A probabilistic approach to robot trajectory generation. In International conference on humanoid robots (humanoids) (pp. 477–483) Righetti, L., & Ijspeert, A. J. (2006). Programmable central pattern generators: An application to biped locomotion control. In International conference on robotics and automation, (ICRA) (pp. 1585–1590). Rozo, L., Calinon, S., Caldwell, D., Jiménez, P., & Torras, C. (2013). Learning collaborative impedance-based robot behaviors. In AAAI conference on artificial intelligence (pp. 1422–1428). NakanishiJMorimotoJEndoGChengGSchaalSKawatoMLearning from demonstration and adaptation of biped locomotionRobotics and Autonomous Systems200447799110.1016/j.robot.2004.03.003 MoroFLTsagarakisNGCaldwellDGOn the kinematic motion primitives (kMPs)—Theory and applicationFrontiers in Neurorobotics2012610118 Lazaric, A., & Ghavamzadeh, M. (2010). Bayesian multi-task reinforcement learning. In International conference on machine learning (ICML) (pp. 599–606). PetersJMistryMUdwadiaFENakanishiJSchaalSA unifying methodology for robot control with redundant DOFsAutonomous Robots200824111210.1007/s10514-007-9051-x StengelRFOptimal control and estimation2012North Chelmsford, MACourier Corporation0854.93001 IjspeertAJNakanishiJHoffmannHPastorPSchaalSDynamical movement primitives: Learning attractor models for motor behaviorsNeural Computation2013252328373305849910.1162/NECO_a_003931269.92002 Toussaint, M. (2009). Robot trajectory optimization using approximate inference. In International conference on machine learning (ICML) (pp. 1049–1056). dAvellaABizziEShared and specific muscle synergies in natural motor behaviorsProceedings of the National Academy of Sciences (PNAS)200510233076308110.1073/pnas.0500199102 Maeda, G., Ewerton, M., Lioutikov, R., Amor, H., Peters, J., & Neumann, G. (2014). Learning interaction for collaborative tasks with probabilistic movement primitives. In International conference on humanoid robots (Humanoids) (pp. 527–534). Ijspeert, A. J., Nakanishi, J., & Schaal, S. (2003). Learning attractor landscapes for learning motor primitives. In Advances in neural information processing systems (NIPS) (pp. 1547–1554). MatsubaraTHyonSHMorimotoJLearning parametric dynamic movement primitives from multiple demonstrationsNeural Networks201124549350010.1016/j.neunet.2011.02.004 OHagan, A., & Forster, J. (2004). Kendalls advanced theory of statistics: Bayesian inference (2nd ed.). Arnold, New York. Technical report, ISBN 0-340-80752-0. Khansari-ZadehSMBillardALearning stable nonlinear dynamical systems with Gaussian mixture modelsIEEE Transactions on Robotics201127594395710.1109/TRO.2011.2159412 IjspeertAJCentral pattern generators for locomotion control in animals and robots: A reviewNeural Networks200821464265310.1016/j.neunet.2008.03.014 BuchliJStulpFTheodorouESchaalSLearning variable impedance controlInternational Journal of Robotics Research201130782083310.1177/0278364911402527 RückertEANeumannGToussaintMMaassWLearned graphical models for probabilistic planning provide a new class of movement primitivesFrontiers in Computational Neuroscience20126971 Schaal, S., Peters, J., Nakanishi, J., & Ijspeert, A. (2005). Learning movement primitives. In International symposium on robotics research (pp. 561–572). Kormushev, P., Calinon, S., & Caldwell, D. G. (2010). Robot motor skill coordination with EM-based reinforcement learning. In International conference on intelligent robots and systems (IROS) (pp. 3232–3237). StarkHWoodsJProbability and random processes with applications to signal processing20013Upper Saddle RiverPrentice-Hall DominiciNIvanenkoYPCappelliniGdAvellaAMondìVCiccheseMFabianoASileiTDi PaoloAGianniniCLocomotor primitives in newborn babies and their developmentScience2011334605899799910.1126/science.1210617 Khansari-Zadeh, S. M., Kronander, K., & Billard, A. (2014). Modeling robot discrete movements with state-varying stiffness and damping: A framework for integrated motion generation and impedance control. In Robotics science and systems (R:SS). GamsANemecBIjspeertAJUdeACoupling movement primitives: Interaction with the environment and bimanual tasksIEEE Transactions on Robotics201430481683010.1109/TRO.2014.2304775 Ernesti, J., Righetti, L., Do, M., Asfour, T., & Schaal, S. (2012). Encoding of periodic and their transient motions by a single dynamic movement primitive. In IEEE-RAS international conference on humanoid robots (humanoids) (pp. 57–64). Kober, J., Muelling, K., Kroemer, O., Lampert, C. H., Scholkopf, B., & Peters, J. (2010). Movement templates for learning of hitting and batting. In International conference on robotics and automation (ICRA) (pp. 853–858). Ewerton, M., Maeda, G., Peters, J., & Neumann, G. (2015). Learning motor skills from partially observed movements executed at different speeds. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 456–463). NeumannGDanielCParaschosAKupcsikAPetersJLearning modular policies for roboticsFrontiers in Computational Neuroscience20148621 KonidarisGKuindersmaSGrupenRBartoARobot learning from demonstration by constructing skill treesInternational Journal of Robotics Research (IJRR)201231336037510.1177/0278364911428653 Bruno, D., Calinon, S., Malekzadeh, M. S., & Caldwell, D. G. (2015). Learning the stiffness of a continuous soft manipulator from multiple demonstrations. In Intelligent robotics and applications (pp. 185–195). Pastor, P., Hoffmann, H., Asfour, T., & Schaal, S. (2009). Learning and generalization of motor skills by learning from demonstration. In International conference on robotics and automation (ICRA) (pp. 763–768) SchaalSMohajerianPIjspeertADynamics systems vs. optimal control—A unifying viewComputational Neuroscience: Theoretical Insights into Brain Function2007165425445 Daniel, C., Neumann, G., & Peters, J. (2012). Learning concurrent motor skills in versatile solution spaces. In IEEE/RSJ international conference on intelligent robots and systems (IROS), (pp. 3591–3597). TodorovEGeneral duality between optimal control and estimationConference on Decision and Control2008542864292 DegallierSRighettiLGaySIjspeertAToward simple control for complex, autonomous robotic applications: Combining discrete and rhythmic motor primitivesAutonomous Robots20113115518110.1007/s10514-011-9235-2 Pastor, P., Righetti, L., Kalakrishnan, M., & Schaal, S. (2011). Online movement adaptation based on previous sensor experiences. In International conference on intelligent robots and systems (IROS) (pp. 365–371) Williams B., Toussaint, M., & Storkey, A. (2007). Modelling motion primitives and their timing in biologically executed movements. In Advances in neural information processing systems (NIPS) (pp. 1609–1616). MuellingKKoberJPetersJA biomimetic approach to robot table tennisAdaptive Behavior Journal201119535937610.1177/1059712311419378 Klug, S., Lens, T., von Stryk, O., Möhl, B., & Karguth, A. (2008). Biologically inspired robot manipulator for new applications in automation engineering. In Proceedings of robotik. da Silva, B., Konidaris, G., & Barto, A. (2012). Learning parameterized skills. In International conference on machine learning (pp. 1679–1686). KulviciusTNingKTamosiunaiteMWorgotterFJoining movement sequences: Modified dynamic movement primitives for robotics applications exemplified on handwritingIEEE Transactions on Robotics201228114515710.1109/TRO.2011.2163863 Li, W., & Todorov, E. (2010). Iterative linear quadratic regulator design for nonlinear biological movement systems. In International conference on informatics in control, automation and robotics (ICINCO) (pp. 222–229). Paraschos, A., Daniel, C., Peters, J., & Neumann, G. (2013a). Probabilistic movement primitives. In Advances in neural information processing systems (NIPS) (pp. 2616–2624). TodorovEJordanMOptimal feedback control as a theory of motor coordinationNature Neuroscience200251226123510.1038/nn963 CalinonSD’HalluinFSauserELCaldwellDGBillardAGLearning and reproduction of gestures by imitationIEEE Robotics and Automation Magazine201017445410.1109/MRA.2010.936947 UdeAGamsAAsfourTMorimotoJTask-specific generalization of discrete and periodic dynamic movement primitivesTransactions in Robotics2010580081510.1109/TRO.2010.2065430 AJ Ijspeert (9648_CR16) 2008; 21 9648_CR52 K Muelling (9648_CR31) 2011; 19 9648_CR12 G Konidaris (9648_CR23) 2012; 31 9648_CR54 9648_CR11 EA Rückert (9648_CR44) 2012; 6 AJ Ijspeert (9648_CR17) 2013; 25 H Stark (9648_CR48) 2001 S Degallier (9648_CR9) 2011; 31 9648_CR18 9648_CR42 T Matsubara (9648_CR29) 2011; 24 9648_CR40 9648_CR45 J Nakanishi (9648_CR33) 2004; 47 9648_CR43 J Peters (9648_CR41) 2008; 24 S Schaal (9648_CR46) 2007; 165 RF Stengel (9648_CR49) 2012 K Mülling (9648_CR32) 2013; 32 E Todorov (9648_CR50) 2008; 5 9648_CR47 N Dominici (9648_CR10) 2011; 334 9648_CR35 NJ Higham (9648_CR15) 1988; 103 G Neumann (9648_CR34) 2014; 8 9648_CR38 9648_CR39 9648_CR36 9648_CR37 A Ude (9648_CR53) 2010; 5 S Calinon (9648_CR4) 2010; 17 9648_CR20 FL Moro (9648_CR30) 2012; 6 A dAvella (9648_CR8) 2005; 102 A Gams (9648_CR14) 2014; 30 9648_CR24 T Kulvicius (9648_CR25) 2012; 28 9648_CR21 9648_CR22 J Buchli (9648_CR2) 2011; 30 SM Khansari-Zadeh (9648_CR19) 2011; 27 S Calinon (9648_CR3) 2016; 9 9648_CR1 D Forte (9648_CR13) 2012; 60 E Todorov (9648_CR51) 2002; 5 9648_CR5 9648_CR27 9648_CR6 9648_CR28 9648_CR7 9648_CR26 |
| References_xml | – reference: ForteDGamsAMorimotoJUdeAOn-line motion synthesis and adaptation using a trajectory databaseRobotics and Autonomous Systems2012601327133910.1016/j.robot.2012.05.004 – reference: Calinon, S., Sardellitti, I., & Caldwell, D. G. (2010b). Learning-based control strategy for safe human–robot interaction exploiting task and robot redundancies. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 249–254). – reference: Klug, S., Lens, T., von Stryk, O., Möhl, B., & Karguth, A. (2008). Biologically inspired robot manipulator for new applications in automation engineering. In Proceedings of robotik. – reference: Righetti, L., & Ijspeert, A. J. (2006). Programmable central pattern generators: An application to biped locomotion control. In International conference on robotics and automation, (ICRA) (pp. 1585–1590). – reference: dAvellaABizziEShared and specific muscle synergies in natural motor behaviorsProceedings of the National Academy of Sciences (PNAS)200510233076308110.1073/pnas.0500199102 – reference: SchaalSMohajerianPIjspeertADynamics systems vs. optimal control—A unifying viewComputational Neuroscience: Theoretical Insights into Brain Function2007165425445 – reference: IjspeertAJCentral pattern generators for locomotion control in animals and robots: A reviewNeural Networks200821464265310.1016/j.neunet.2008.03.014 – reference: Neumann, G., Maass, W., & Peters, J. (2009). Learning complex motions by sequencing simpler motion templates. In International conference on machine learning (ICML) (pp. 753–760) – reference: Khansari-Zadeh, S. M., Kronander, K., & Billard, A. (2014). Modeling robot discrete movements with state-varying stiffness and damping: A framework for integrated motion generation and impedance control. In Robotics science and systems (R:SS). – reference: Paraschos, A., Daniel, C., Peters, J., & Neumann, G. (2013a). Probabilistic movement primitives. In Advances in neural information processing systems (NIPS) (pp. 2616–2624). – reference: Daniel, C., Neumann, G., & Peters, J. (2012). Learning concurrent motor skills in versatile solution spaces. In IEEE/RSJ international conference on intelligent robots and systems (IROS), (pp. 3591–3597). – reference: MüllingKKoberJKroemerOPetersJLearning to select and generalize striking movements in robot table tennisThe International Journal of Robotics Research201332326327910.1177/0278364912472380 – reference: PetersJMistryMUdwadiaFENakanishiJSchaalSA unifying methodology for robot control with redundant DOFsAutonomous Robots200824111210.1007/s10514-007-9051-x – reference: Maeda, G., Ewerton, M., Lioutikov, R., Amor, H., Peters, J., & Neumann, G. (2014). Learning interaction for collaborative tasks with probabilistic movement primitives. In International conference on humanoid robots (Humanoids) (pp. 527–534). – reference: GamsANemecBIjspeertAJUdeACoupling movement primitives: Interaction with the environment and bimanual tasksIEEE Transactions on Robotics201430481683010.1109/TRO.2014.2304775 – reference: MoroFLTsagarakisNGCaldwellDGOn the kinematic motion primitives (kMPs)—Theory and applicationFrontiers in Neurorobotics2012610118 – reference: Pastor, P., Hoffmann, H., Asfour, T., & Schaal, S. (2009). Learning and generalization of motor skills by learning from demonstration. In International conference on robotics and automation (ICRA) (pp. 763–768) – reference: Bruno, D., Calinon, S., Malekzadeh, M. S., & Caldwell, D. G. (2015). Learning the stiffness of a continuous soft manipulator from multiple demonstrations. In Intelligent robotics and applications (pp. 185–195). – reference: Rueckert, E., Mundo, J., Paraschos, A., Peters, J., & Neumann, G. (2015). Extracting low-dimensional control variables for movement primitives. In International conference on robotics and automation (ICRA) (pp. 1511–1518). – reference: KonidarisGKuindersmaSGrupenRBartoARobot learning from demonstration by constructing skill treesInternational Journal of Robotics Research (IJRR)201231336037510.1177/0278364911428653 – reference: TodorovEJordanMOptimal feedback control as a theory of motor coordinationNature Neuroscience200251226123510.1038/nn963 – reference: TodorovEGeneral duality between optimal control and estimationConference on Decision and Control2008542864292 – reference: Kober, J., Muelling, K., Kroemer, O., Lampert, C. H., Scholkopf, B., & Peters, J. (2010). Movement templates for learning of hitting and batting. In International conference on robotics and automation (ICRA) (pp. 853–858). – reference: Ijspeert, A. J., Nakanishi, J., & Schaal, S. (2003). Learning attractor landscapes for learning motor primitives. In Advances in neural information processing systems (NIPS) (pp. 1547–1554). – reference: NeumannGDanielCParaschosAKupcsikAPetersJLearning modular policies for roboticsFrontiers in Computational Neuroscience20148621 – reference: DominiciNIvanenkoYPCappelliniGdAvellaAMondìVCiccheseMFabianoASileiTDi PaoloAGianniniCLocomotor primitives in newborn babies and their developmentScience2011334605899799910.1126/science.1210617 – reference: StengelRFOptimal control and estimation2012North Chelmsford, MACourier Corporation0854.93001 – reference: MuellingKKoberJPetersJA biomimetic approach to robot table tennisAdaptive Behavior Journal201119535937610.1177/1059712311419378 – reference: DegallierSRighettiLGaySIjspeertAToward simple control for complex, autonomous robotic applications: Combining discrete and rhythmic motor primitivesAutonomous Robots20113115518110.1007/s10514-011-9235-2 – reference: Lazaric, A., & Ghavamzadeh, M. (2010). Bayesian multi-task reinforcement learning. In International conference on machine learning (ICML) (pp. 599–606). – reference: OHagan, A., & Forster, J. (2004). Kendalls advanced theory of statistics: Bayesian inference (2nd ed.). Arnold, New York. Technical report, ISBN 0-340-80752-0. – reference: Rozo, L., Calinon, S., Caldwell, D., Jiménez, P., & Torras, C. (2013). Learning collaborative impedance-based robot behaviors. In AAAI conference on artificial intelligence (pp. 1422–1428). – reference: RückertEANeumannGToussaintMMaassWLearned graphical models for probabilistic planning provide a new class of movement primitivesFrontiers in Computational Neuroscience20126971 – reference: BuchliJStulpFTheodorouESchaalSLearning variable impedance controlInternational Journal of Robotics Research201130782083310.1177/0278364911402527 – reference: da Silva, B., Konidaris, G., & Barto, A. (2012). Learning parameterized skills. In International conference on machine learning (pp. 1679–1686). – reference: Williams B., Toussaint, M., & Storkey, A. (2007). Modelling motion primitives and their timing in biologically executed movements. In Advances in neural information processing systems (NIPS) (pp. 1609–1616). – reference: Ewerton, M., Maeda, G., Peters, J., & Neumann, G. (2015). Learning motor skills from partially observed movements executed at different speeds. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 456–463). – reference: StarkHWoodsJProbability and random processes with applications to signal processing20013Upper Saddle RiverPrentice-Hall – reference: Ernesti, J., Righetti, L., Do, M., Asfour, T., & Schaal, S. (2012). Encoding of periodic and their transient motions by a single dynamic movement primitive. In IEEE-RAS international conference on humanoid robots (humanoids) (pp. 57–64). – reference: Kormushev, P., Calinon, S., & Caldwell, D. G. (2010). Robot motor skill coordination with EM-based reinforcement learning. In International conference on intelligent robots and systems (IROS) (pp. 3232–3237). – reference: UdeAGamsAAsfourTMorimotoJTask-specific generalization of discrete and periodic dynamic movement primitivesTransactions in Robotics2010580081510.1109/TRO.2010.2065430 – reference: KulviciusTNingKTamosiunaiteMWorgotterFJoining movement sequences: Modified dynamic movement primitives for robotics applications exemplified on handwritingIEEE Transactions on Robotics201228114515710.1109/TRO.2011.2163863 – reference: Li, W., & Todorov, E. (2010). Iterative linear quadratic regulator design for nonlinear biological movement systems. In International conference on informatics in control, automation and robotics (ICINCO) (pp. 222–229). – reference: Pastor, P., Righetti, L., Kalakrishnan, M., & Schaal, S. (2011). Online movement adaptation based on previous sensor experiences. In International conference on intelligent robots and systems (IROS) (pp. 365–371) – reference: Schaal, S., Peters, J., Nakanishi, J., & Ijspeert, A. (2005). Learning movement primitives. In International symposium on robotics research (pp. 561–572). – reference: MatsubaraTHyonSHMorimotoJLearning parametric dynamic movement primitives from multiple demonstrationsNeural Networks201124549350010.1016/j.neunet.2011.02.004 – reference: Paraschos, A., Neumann, G., & Peters, J. (2013b). A probabilistic approach to robot trajectory generation. In International conference on humanoid robots (humanoids) (pp. 477–483) – reference: Toussaint, M. (2009). Robot trajectory optimization using approximate inference. In International conference on machine learning (ICML) (pp. 1049–1056). – reference: CalinonSA tutorial on task-parameterized movement learning and retrievalIntelligent Service Robotics20169112910.1007/s11370-015-0187-9 – reference: HighamNJComputing a nearest symmetric positive semidefinite matrixLinear Algebra and its Applications198810310311894399710.1016/0024-3795(88)90223-60649.65026 – reference: CalinonSD’HalluinFSauserELCaldwellDGBillardAGLearning and reproduction of gestures by imitationIEEE Robotics and Automation Magazine201017445410.1109/MRA.2010.936947 – reference: Khansari-ZadehSMBillardALearning stable nonlinear dynamical systems with Gaussian mixture modelsIEEE Transactions on Robotics201127594395710.1109/TRO.2011.2159412 – reference: IjspeertAJNakanishiJHoffmannHPastorPSchaalSDynamical movement primitives: Learning attractor models for motor behaviorsNeural Computation2013252328373305849910.1162/NECO_a_003931269.92002 – reference: NakanishiJMorimotoJEndoGChengGSchaalSKawatoMLearning from demonstration and adaptation of biped locomotionRobotics and Autonomous Systems200447799110.1016/j.robot.2004.03.003 – volume: 9 start-page: 1 issue: 1 year: 2016 ident: 9648_CR3 publication-title: Intelligent Service Robotics doi: 10.1007/s11370-015-0187-9 – volume-title: Optimal control and estimation year: 2012 ident: 9648_CR49 – ident: 9648_CR38 doi: 10.1109/HUMANOIDS.2013.7030017 – ident: 9648_CR26 – volume: 32 start-page: 263 issue: 3 year: 2013 ident: 9648_CR32 publication-title: The International Journal of Robotics Research doi: 10.1177/0278364912472380 – ident: 9648_CR42 doi: 10.1109/ROBOT.2006.1641933 – volume: 19 start-page: 359 issue: 5 year: 2011 ident: 9648_CR31 publication-title: Adaptive Behavior Journal doi: 10.1177/1059712311419378 – volume: 21 start-page: 642 issue: 4 year: 2008 ident: 9648_CR16 publication-title: Neural Networks doi: 10.1016/j.neunet.2008.03.014 – ident: 9648_CR24 doi: 10.1109/IROS.2010.5649089 – volume: 25 start-page: 328 issue: 2 year: 2013 ident: 9648_CR17 publication-title: Neural Computation doi: 10.1162/NECO_a_00393 – ident: 9648_CR52 doi: 10.1145/1553374.1553508 – ident: 9648_CR5 doi: 10.1109/IROS.2010.5648931 – ident: 9648_CR7 – ident: 9648_CR11 doi: 10.1109/HUMANOIDS.2012.6651499 – volume: 27 start-page: 943 issue: 5 year: 2011 ident: 9648_CR19 publication-title: IEEE Transactions on Robotics doi: 10.1109/TRO.2011.2159412 – volume: 17 start-page: 44 year: 2010 ident: 9648_CR4 publication-title: IEEE Robotics and Automation Magazine doi: 10.1109/MRA.2010.936947 – ident: 9648_CR36 – ident: 9648_CR21 – volume: 24 start-page: 493 issue: 5 year: 2011 ident: 9648_CR29 publication-title: Neural Networks doi: 10.1016/j.neunet.2011.02.004 – ident: 9648_CR1 doi: 10.1007/978-3-319-22873-0_17 – volume: 30 start-page: 820 issue: 7 year: 2011 ident: 9648_CR2 publication-title: International Journal of Robotics Research doi: 10.1177/0278364911402527 – volume: 5 start-page: 800 year: 2010 ident: 9648_CR53 publication-title: Transactions in Robotics doi: 10.1109/TRO.2010.2065430 – volume: 165 start-page: 425 year: 2007 ident: 9648_CR46 publication-title: Computational Neuroscience: Theoretical Insights into Brain Function – ident: 9648_CR40 doi: 10.1109/IROS.2011.6095059 – volume: 24 start-page: 1 issue: 1 year: 2008 ident: 9648_CR41 publication-title: Autonomous Robots doi: 10.1007/s10514-007-9051-x – volume: 31 start-page: 360 issue: 3 year: 2012 ident: 9648_CR23 publication-title: International Journal of Robotics Research (IJRR) doi: 10.1177/0278364911428653 – ident: 9648_CR12 doi: 10.1109/IROS.2015.7353412 – volume: 6 start-page: 1 issue: 10 year: 2012 ident: 9648_CR30 publication-title: Frontiers in Neurorobotics – ident: 9648_CR47 doi: 10.1007/11008941_60 – ident: 9648_CR6 doi: 10.1109/IROS.2012.6386047 – volume: 6 start-page: 1 issue: 97 year: 2012 ident: 9648_CR44 publication-title: Frontiers in Computational Neuroscience – ident: 9648_CR22 doi: 10.1109/ROBOT.2010.5509672 – volume: 47 start-page: 79 year: 2004 ident: 9648_CR33 publication-title: Robotics and Autonomous Systems doi: 10.1016/j.robot.2004.03.003 – ident: 9648_CR28 doi: 10.1109/HUMANOIDS.2014.7041413 – ident: 9648_CR18 – ident: 9648_CR20 – volume: 5 start-page: 1226 year: 2002 ident: 9648_CR51 publication-title: Nature Neuroscience doi: 10.1038/nn963 – volume: 102 start-page: 3076 issue: 3 year: 2005 ident: 9648_CR8 publication-title: Proceedings of the National Academy of Sciences (PNAS) doi: 10.1073/pnas.0500199102 – volume: 28 start-page: 145 issue: 1 year: 2012 ident: 9648_CR25 publication-title: IEEE Transactions on Robotics doi: 10.1109/TRO.2011.2163863 – ident: 9648_CR43 doi: 10.1609/aaai.v27i1.8543 – volume: 5 start-page: 4286 year: 2008 ident: 9648_CR50 publication-title: Conference on Decision and Control – ident: 9648_CR39 doi: 10.1109/ROBOT.2009.5152385 – volume: 8 start-page: 1 issue: 62 year: 2014 ident: 9648_CR34 publication-title: Frontiers in Computational Neuroscience – ident: 9648_CR45 doi: 10.1109/ICRA.2015.7139390 – ident: 9648_CR35 doi: 10.1145/1553374.1553471 – volume: 60 start-page: 1327 year: 2012 ident: 9648_CR13 publication-title: Robotics and Autonomous Systems doi: 10.1016/j.robot.2012.05.004 – volume: 103 start-page: 103 year: 1988 ident: 9648_CR15 publication-title: Linear Algebra and its Applications doi: 10.1016/0024-3795(88)90223-6 – volume: 30 start-page: 816 issue: 4 year: 2014 ident: 9648_CR14 publication-title: IEEE Transactions on Robotics doi: 10.1109/TRO.2014.2304775 – ident: 9648_CR27 – volume-title: Probability and random processes with applications to signal processing year: 2001 ident: 9648_CR48 – volume: 31 start-page: 155 year: 2011 ident: 9648_CR9 publication-title: Autonomous Robots doi: 10.1007/s10514-011-9235-2 – volume: 334 start-page: 997 issue: 6058 year: 2011 ident: 9648_CR10 publication-title: Science doi: 10.1126/science.1210617 – ident: 9648_CR54 – ident: 9648_CR37 |
| SSID | ssj0009700 |
| Score | 2.6076021 |
| Snippet | Movement Primitives are a well-established paradigm for modular movement representation and generation. They provide a data-driven representation of movements... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 529 |
| SubjectTerms | Artificial Intelligence Computer Imaging Computer simulation Control Engineering Feedback control Mechatronics Pattern Recognition and Graphics Probability theory Representations Robotics Robotics and Automation Robots Vision |
| SummonAdditionalLinks | – databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3dS8MwED90-qAPfovTKX3wSQlsTZo0jyIOH2QI6thbaD4KA-3GVv37vWStnaKCvraXUO7Su9-Ru98BnCOix6zLaOKkdITZ2BFE9ZawzEltmDRxmEM2vBODQToayfuqj3teV7vXV5LBUy81u2FwJ96rSs5SIlZhLfFkMz5Ffxg2TLtV3wnGfYJwmdZXmd9t8TkYNQjzy6VoiDX97X995Q5sVdAyulqchV1YccUebC4RDu4DDRUCkZ8iE5h1PUlz9DIJnOElPvbdTp6HNhoXEcpMPIfzATz1bx6vb0k1NoEY2uMlYbGOhc0SahOHgclo7npdk2jGRC6oyZ1knrMecV-ScKa5xRTPcNs12mQZpnP0EFrFpHBHEGmtbYwBPpVGMp6K1NGc5qh1NCFii6wN3Vp_ylSc4n60xbNq2JC9PhTqQ3l9KNGGi48l0wWhxm_Cndooqvq35go9kESQi7liGy5rIzSvf9zs-E_SJ7CB2ChdlJt1oFXOXt0prJu3cjyfnYUj9w68MM6D priority: 102 providerName: Springer Nature |
| Title | Using probabilistic movement primitives in robotics |
| URI | https://link.springer.com/article/10.1007/s10514-017-9648-7 https://www.proquest.com/docview/2259020282 |
| Volume | 42 |
| WOSCitedRecordID | wos000425113800002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1573-7527 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009700 issn: 0929-5593 databaseCode: RSV dateStart: 19970301 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV07T8MwED5BywADb0ShVBmYQBZt7Dw8IUCtGFBVtVBVLFHsuFIlSEob-P2cHYcAEl1YPCSOFZ3tu-_su-8AzhHRo9clBVGcK8ISVxFE9QlhseJCMi5dU4ds_BD0--Fkwgf2wG1pwypLnWgUdZJJfUZ-heuOI7RBD-F6_kZ01Sh9u2pLaKxDXbMkdEzo3qgi3bUpKAgBCCJnWt5qFqlzCBWI1tHcZyEJftqlCmz-uh81Zqe3898f3oVtCzidm2KF7MGaSvdh6xsN4QFQEzfg6Noyhm9XUzc7r5lhEs_xsc6B0uy0zix1sE-mmZ0P4anXfby7J7aYApG04-eEucINktijiafQXEnhq05beoKxYBpQOVWcaSZ7RIOe5zPhJ-j4ST9pSyHjGJ08egS1NEvVMThCiMRFsx9yyZkfBqGiUzpF5IETi4gjbkC7FGUkLdO4LnjxElUcyVr6EUo_0tKPggZcfH0yL2g2VnVulhKP7I5bRpW4G3BZzln1-s_BTlYPdgqbCJHCIuqsCbV88a7OYEN-5LPlogX1225_MGyZZYftwHvGdjgafwLDWtyb |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT8JAEJ4gmKgH30YUtQe9aDZCd_vYgzE-IBCQEIOGW-1ul4REAQE1_il_o7N9WDWRGwev7e6m6Ted_aaz8w3AITJ6jLqkIIpzRVhgKoKsPiDMV1xIxqUZ9iG7bzjNptvp8FYGPpJaGH2sMvGJoaMOBlL_Iz9Fu-NIbTBCOB8-E901SmdXkxYakVnU1fsbhmzjs9o14ntkmpVy-6pK4q4CRNKSPSHMFKYT-BYNLIV-WwpblYrSEow5XYfKruJMS7ojLbIsmwk7wAhI2kFRCun7GO1QXHcOcowy28pC7rLcbN2mMr9x0QuSDoJcnSZ51KhYD8kJ0bsCt5lLnJ87YUpvf2Vkw42usvLfXtEqLMeU2riIvoE1yKj-Oix9E1rcABqejDB095xQUViLUxtPg1ArfYKXdZWX1t81en0Dxwy0dvUm3M3kqbcg2x_01TYYQojARGLjcsmZ7Tquol3aRW6Fpoucys9DMYHOk7GWum7p8eilKtAabQ_R9jTanpOH468pw0hIZNrgQoKwF_uUsZfCm4eTxEbS238utjN9sQNYqLZvGl6j1qzvwiISQjc6Y1eA7GT0ovZgXr5OeuPRfmzsBjzM2ng-AeClNgg |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT8JAEJ4gGqMH30YUtQe9aDZAd_vYgzEqEAmEEKOGW-1utwmJAgJq_Gv-Omf7sGoiNw5e291N0286-0135huAI2T0GHVJQRTnirDAVARZfUCYr7iQjEsz6kN233Labbfb5Z0cfKS1MDqtMvWJkaMOBlL_Iy-h3XGkNhghlMIkLaJTrZ8Pn4nuIKVPWtN2GrGJNNX7G4Zv47NGFbE-Ns167fbqmiQdBoikFXtCmClMJ_AtGlgKfbgUtqqUpSUYc0KHylBxpuXdkSJZls2EHWA0JO2gLIX0fYx8KK47B_MOxaAnD_OXtXbnJpP8TQpgkIAQ5O00PVONC_eQqBC9Q3CbucT5uStmVPfX6Wy06dVX__PrWoOVhGobF_G3sQ451d-A5W8CjJtAo4wJQ3fViZSGtWi18TSINNQneFlXf2ldXqPXN3DMQGtab8HdTJ56G_L9QV_tgCGECEwkPC6XnNmu4yoa0hA5F5o0ci2_AOUURk8mGuu61cejl6lDa-Q9RN7TyHtOAU6-pgxjgZFpg4sp2l7ia8ZeBnUBTlN7yW7_udju9MUOYREtxms12s09WEKe6Mapd0XIT0Yvah8W5OukNx4dJHZvwMOsbecTlvo-yw |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Using+probabilistic+movement+primitives+in+robotics&rft.jtitle=Autonomous+robots&rft.au=Paraschos%2C+Alexandros&rft.au=Christian%2C+Daniel&rft.au=Peters%2C+Jan&rft.au=Neumann%2C+Gerhard&rft.date=2018-03-01&rft.pub=Springer+Nature+B.V&rft.issn=0929-5593&rft.eissn=1573-7527&rft.volume=42&rft.issue=3&rft.spage=529&rft.epage=551&rft_id=info:doi/10.1007%2Fs10514-017-9648-7&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0929-5593&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0929-5593&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0929-5593&client=summon |