A novel framework for generalizing dynamic movement primitives under kinematic constraints
In this work, we propose a novel framework for generalizing a desired trajectory pattern, encoded using Dynamic Movement Primitives (DMP), subject to kinematic constraints. DMP have been extensively used in robotics for encoding and reproducing kinematic behaviours, thanks to their generalization, s...
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| Published in | Autonomous robots Vol. 47; no. 1; pp. 37 - 50 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.01.2023
Springer Nature B.V |
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| Abstract | In this work, we propose a novel framework for generalizing a desired trajectory pattern, encoded using Dynamic Movement Primitives (DMP), subject to kinematic constraints. DMP have been extensively used in robotics for encoding and reproducing kinematic behaviours, thanks to their generalization, stability and robustness properties. However, incorporating kinematic constraints has not yet been fully addressed. To this end, we design an optimization framework, based on the DMP formulation from our previous work, for generalizing trajectory patterns, encoded with DMP subject to kinematic constraints, considering also time-varying target and time duration, via-point and obstacle constraints. Simulations highlight these properties and comparisons are drawn with other approaches for enforcing constraints on DMP. The usefulness and applicability of the proposed framework is showcased in experimental scenarios, including a handover, where the target and time duration vary, and placing scenarios, where obstacles are dynamically introduced in the scene. |
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| AbstractList | In this work, we propose a novel framework for generalizing a desired trajectory pattern, encoded using Dynamic Movement Primitives (DMP), subject to kinematic constraints. DMP have been extensively used in robotics for encoding and reproducing kinematic behaviours, thanks to their generalization, stability and robustness properties. However, incorporating kinematic constraints has not yet been fully addressed. To this end, we design an optimization framework, based on the DMP formulation from our previous work, for generalizing trajectory patterns, encoded with DMP subject to kinematic constraints, considering also time-varying target and time duration, via-point and obstacle constraints. Simulations highlight these properties and comparisons are drawn with other approaches for enforcing constraints on DMP. The usefulness and applicability of the proposed framework is showcased in experimental scenarios, including a handover, where the target and time duration vary, and placing scenarios, where obstacles are dynamically introduced in the scene. |
| Author | Sidiropoulos, Antonis Doulgeri, Zoe Papageorgiou, Dimitrios |
| Author_xml | – sequence: 1 givenname: Antonis orcidid: 0000-0002-6089-5980 surname: Sidiropoulos fullname: Sidiropoulos, Antonis email: antosidi@ece.auth.gr organization: Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki – sequence: 2 givenname: Dimitrios orcidid: 0000-0003-0361-8757 surname: Papageorgiou fullname: Papageorgiou, Dimitrios organization: Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki – sequence: 3 givenname: Zoe orcidid: 0000-0003-2188-9358 surname: Doulgeri fullname: Doulgeri, Zoe organization: Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki |
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| CitedBy_id | crossref_primary_10_1016_j_rcim_2024_102817 crossref_primary_10_1007_s10846_024_02051_0 crossref_primary_10_3390_electronics13193860 crossref_primary_10_3390_robotics13070100 |
| Cites_doi | 10.1016/j.rcim.2021.102223 10.1109/ICRA48506.2021.9562059 10.1177/0278364912472380 10.1109/ICRA.2014.6907265 10.1109/IROS45743.2020.9341728 10.1007/s12532-020-00179-2 10.1109/HUMANOIDS.2018.8624934 10.1007/978-3-540-30301-5_60 10.1109/HUMANOIDS.2014.7041413 10.1007/s10514-017-9648-7 10.1162/NECO_a_00393 10.1109/ROBOT.2009.5152423 10.1109/ICRA48506.2021.9561120 10.1109/IROS51168.2021.9636674 10.23919/ECC.2019.8796278 10.1109/ICRA.2015.7139285 10.1007/s10846-014-0100-3 10.1109/LCSYS.2019.2946761 10.1109/TRO.2021.3127108 10.1007/s10514-009-9118-y 10.1109/RO-MAN47096.2020.9223579 10.1017/S0263574717000133 10.1109/ROBOT.2010.5509672 10.1109/RO-MAN47096.2020.9223500 10.1109/IROS.2016.7759617 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2022. corrected publication 2023 The Author(s) 2022. corrected publication 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| DOI | 10.1007/s10514-022-10067-4 |
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| Keywords | Dynamic movement primitives Constrained motion generation Online trajectory adaptation Constrained optimization |
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| References | Kober, J., Mülling, K., Krömer, O., Lampert, C.H., Schölkopf, B., Peters, J. (2010). Movement templates for learning of hitting and batting. In 2010 IEEE International Conference on Robotics and Automation pp. 853–858 https://doi.org/10.1109/ROBOT.2010.5509672 DahlinAKarayiannidisYAdaptive trajectory generation under velocity constraints using dynamical movement primitivesIEEE Control Systems Letters202042438443421132410.1109/LCSYS.2019.2946761 Kastritsi, T., Papageorgiou, D., Sarantopoulos, I., Doulgeri, Z., Rovithakis, G.A. (2019). Stability of active constraints enforcement in sensitive regions defined by point-clouds for robotic surgical procedures. In 2019 18th European Control Conference (ECC), pp. 1604–1609 https://doi.org/10.23919/ECC.2019.8796278 MüllingKKoberJKroemerOPetersJLearning to select and generalize striking movements in robot table tennisThe International Journal of Robotics Research201332326327910.1177/0278364912472380 Hoffmann, H., Pastor, P., Park, D., Schaal, S. (2009). Biologically-inspired dynamical systems for movement generation: Automatic real-time goal adaptation and obstacle avoidance. In 2009 IEEE International Conference on Robotics and Automation, pp. 2587–2592. Maeda, G., Ewerton, M., Lioutikov, R., Amor, H.B., Peters, J., Neumann, G. (2014). Learning interaction for collaborative tasks with probabilistic movement primitives. In 2014 IEEE-RAS International Conference on Humanoid Robots, pp. 527–534. ParaschosADanielCPetersJNeumannGUsing probabilistic movement primitives in roboticsAutonomous Robots201842352955110.1007/s10514-017-9648-7 Sidiropoulos, A., Doulgeri, Z. (2021). A reversible dynamic movement primitive formulation. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 3147–3153 https://doi.org/10.1109/ICRA48506.2021.9562059 GamsAIjspeertAJSchaalSLenarcicJOn-line learning and modulation of periodic movements with nonlinear dynamical systemsAutonomous Robots20092732310.1007/s10514-009-9118-y Wen, Y., R. Pagilla, P. (2021). Path-constrained optimal trajectory planning for robot manipulators with obstacle avoidance. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (Accepted). Ben Amor, H., Neumann, G., Kamthe, S., Kroemer, O., Peters, J. (2014). Interaction primitives for human-robot cooperation tasks. In 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 2831–2837 https://doi.org/10.1109/ICRA.2014.6907265 Duan, A., Camoriano, R., Ferigo, D., Calandriello, D., Rosasco, L., Pucci, D. (2018). Constrained dmps for feasible skill learning on humanoid robots. In 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), pp. 1–6 https://doi.org/10.1109/HUMANOIDS.2018.8624934 Mghames, S., Hanheide, M., Ghalamzan, E. A.(2020). Interactive movement primitives: Planning to push occluding pieces for fruit picking. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2616–2623 https://doi.org/10.1109/IROS45743.2020.9341728 StellatoBBanjacGGoulartPBemporadABoydSOSQP: An operator splitting solver for quadratic programsMathematical Programming Computation2020124637672416355710.1007/s12532-020-00179-21452.90236 IjspeertAJNakanishiJHoffmannHPastorPSchaalSDynamical movement primitives: Learning attractor models for motor behaviorsNeural Computation2013252328373305849910.1162/NECO_a_003931269.92002 Frank, F., Paraschos, A., & vander Smagt, P., Cseke, B. (2021). Constrained probabilistic movement primitives for robot trajectory adaptation. IEEE Transactions on Robotics. https://doi.org/10.1109/TRO.2021.3127108. KrugRDimitrovDModel predictive motion control based on generalized dynamical movement primitivesJournal of Intelligent & Robotic Systems2015771173510.1007/s10846-014-0100-3 Cardoso, C., Jamone, L., Bernardino, A. (2015) A novel approach to dynamic movement imitation based on quadratic programming. In 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 906–911 . https://doi.org/10.1109/ICRA.2015.7139285 Koutras, L., Doulgeri, Z. (2020). A novel dmp formulation for global and frame independent spatial scaling in the task space. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 727–732 . https://doi.org/10.1109/RO-MAN47096.2020.9223500 Buizza AvanziniGZanchettinAMRoccoPConstrained model predictive control for mobile robotic manipulatorsRobotica2018361193810.1017/S0263574717000133 Billard, A., Calinon, S., Dillmann, R., Schaal, S. (2008). In textitSiciliano, B., Khatib, O. (eds.) Robot Programming by Demonstration, pp. 1371–1394. Springer. https://doi.org/10.1007/978-3-540-30301-5_60 Liang, Y., Li, W., Wang, Y., Xiong, R., Mao, Y., Zhang, J. (2021). Dynamic movement primitive based motion retargeting for dual-arm sign language motions. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 8195–8201 https://doi.org/10.1109/ICRA48506.2021.9561120 Wang, J., Olson, E. (2016). AprilTag 2: Efficient and robust fiducial detection. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4193–4198. IEEE, https://doi.org/10.1109/IROS.2016.7759617 MerckaertKConvensBWuC-JRonconeANicotraMMVanderborghtBReal-time motion control of robotic manipulators for safe human-robot coexistenceRobotics and Computer-Integrated Manufacturing20227310.1016/j.rcim.2021.102223 Papageorgiou, D., Doulgeri, Z. (2020). Learning by demonstration for constrained tasks. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1088–1093 https://doi.org/10.1109/RO-MAN47096.2020.9223579 10067_CR1 AJ Ijspeert (10067_CR10) 2013; 25 10067_CR2 10067_CR9 G Buizza Avanzini (10067_CR3) 2018; 36 K Mülling (10067_CR19) 2013; 32 10067_CR4 10067_CR7 A Gams (10067_CR8) 2009; 27 10067_CR6 A Dahlin (10067_CR5) 2020; 4 10067_CR12 10067_CR11 10067_CR13 10067_CR16 10067_CR15 10067_CR18 R Krug (10067_CR14) 2015; 77 B Stellato (10067_CR23) 2020; 12 K Merckaert (10067_CR17) 2022; 73 A Paraschos (10067_CR21) 2018; 42 10067_CR20 10067_CR22 10067_CR25 10067_CR24 |
| References_xml | – reference: Wen, Y., R. Pagilla, P. (2021). Path-constrained optimal trajectory planning for robot manipulators with obstacle avoidance. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (Accepted). – reference: Kober, J., Mülling, K., Krömer, O., Lampert, C.H., Schölkopf, B., Peters, J. (2010). Movement templates for learning of hitting and batting. In 2010 IEEE International Conference on Robotics and Automation pp. 853–858 https://doi.org/10.1109/ROBOT.2010.5509672 – reference: Ben Amor, H., Neumann, G., Kamthe, S., Kroemer, O., Peters, J. (2014). Interaction primitives for human-robot cooperation tasks. In 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 2831–2837 https://doi.org/10.1109/ICRA.2014.6907265 – reference: StellatoBBanjacGGoulartPBemporadABoydSOSQP: An operator splitting solver for quadratic programsMathematical Programming Computation2020124637672416355710.1007/s12532-020-00179-21452.90236 – reference: Wang, J., Olson, E. (2016). AprilTag 2: Efficient and robust fiducial detection. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4193–4198. IEEE, https://doi.org/10.1109/IROS.2016.7759617 – reference: DahlinAKarayiannidisYAdaptive trajectory generation under velocity constraints using dynamical movement primitivesIEEE Control Systems Letters202042438443421132410.1109/LCSYS.2019.2946761 – reference: Kastritsi, T., Papageorgiou, D., Sarantopoulos, I., Doulgeri, Z., Rovithakis, G.A. (2019). Stability of active constraints enforcement in sensitive regions defined by point-clouds for robotic surgical procedures. In 2019 18th European Control Conference (ECC), pp. 1604–1609 https://doi.org/10.23919/ECC.2019.8796278 – reference: MüllingKKoberJKroemerOPetersJLearning to select and generalize striking movements in robot table tennisThe International Journal of Robotics Research201332326327910.1177/0278364912472380 – reference: Koutras, L., Doulgeri, Z. (2020). A novel dmp formulation for global and frame independent spatial scaling in the task space. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 727–732 . https://doi.org/10.1109/RO-MAN47096.2020.9223500 – reference: Billard, A., Calinon, S., Dillmann, R., Schaal, S. (2008). In textitSiciliano, B., Khatib, O. (eds.) Robot Programming by Demonstration, pp. 1371–1394. Springer. https://doi.org/10.1007/978-3-540-30301-5_60 – reference: MerckaertKConvensBWuC-JRonconeANicotraMMVanderborghtBReal-time motion control of robotic manipulators for safe human-robot coexistenceRobotics and Computer-Integrated Manufacturing20227310.1016/j.rcim.2021.102223 – reference: Buizza AvanziniGZanchettinAMRoccoPConstrained model predictive control for mobile robotic manipulatorsRobotica2018361193810.1017/S0263574717000133 – reference: Hoffmann, H., Pastor, P., Park, D., Schaal, S. (2009). Biologically-inspired dynamical systems for movement generation: Automatic real-time goal adaptation and obstacle avoidance. In 2009 IEEE International Conference on Robotics and Automation, pp. 2587–2592. – reference: ParaschosADanielCPetersJNeumannGUsing probabilistic movement primitives in roboticsAutonomous Robots201842352955110.1007/s10514-017-9648-7 – reference: Cardoso, C., Jamone, L., Bernardino, A. (2015) A novel approach to dynamic movement imitation based on quadratic programming. In 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 906–911 . https://doi.org/10.1109/ICRA.2015.7139285 – reference: Maeda, G., Ewerton, M., Lioutikov, R., Amor, H.B., Peters, J., Neumann, G. (2014). Learning interaction for collaborative tasks with probabilistic movement primitives. In 2014 IEEE-RAS International Conference on Humanoid Robots, pp. 527–534. – reference: Duan, A., Camoriano, R., Ferigo, D., Calandriello, D., Rosasco, L., Pucci, D. (2018). Constrained dmps for feasible skill learning on humanoid robots. In 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), pp. 1–6 https://doi.org/10.1109/HUMANOIDS.2018.8624934 – reference: GamsAIjspeertAJSchaalSLenarcicJOn-line learning and modulation of periodic movements with nonlinear dynamical systemsAutonomous Robots20092732310.1007/s10514-009-9118-y – reference: Papageorgiou, D., Doulgeri, Z. (2020). Learning by demonstration for constrained tasks. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1088–1093 https://doi.org/10.1109/RO-MAN47096.2020.9223579 – reference: Sidiropoulos, A., Doulgeri, Z. (2021). A reversible dynamic movement primitive formulation. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 3147–3153 https://doi.org/10.1109/ICRA48506.2021.9562059 – reference: Frank, F., Paraschos, A., & vander Smagt, P., Cseke, B. (2021). Constrained probabilistic movement primitives for robot trajectory adaptation. IEEE Transactions on Robotics. https://doi.org/10.1109/TRO.2021.3127108. – reference: KrugRDimitrovDModel predictive motion control based on generalized dynamical movement primitivesJournal of Intelligent & Robotic Systems2015771173510.1007/s10846-014-0100-3 – reference: Liang, Y., Li, W., Wang, Y., Xiong, R., Mao, Y., Zhang, J. (2021). Dynamic movement primitive based motion retargeting for dual-arm sign language motions. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 8195–8201 https://doi.org/10.1109/ICRA48506.2021.9561120 – reference: IjspeertAJNakanishiJHoffmannHPastorPSchaalSDynamical movement primitives: Learning attractor models for motor behaviorsNeural Computation2013252328373305849910.1162/NECO_a_003931269.92002 – reference: Mghames, S., Hanheide, M., Ghalamzan, E. A.(2020). Interactive movement primitives: Planning to push occluding pieces for fruit picking. 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| Snippet | In this work, we propose a novel framework for generalizing a desired trajectory pattern, encoded using Dynamic Movement Primitives (DMP), subject to kinematic... |
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| SubjectTerms | Artificial Intelligence Barriers Computer Imaging Constraints Control Design optimization Engineering Kinematics Mechatronics Optimization Pattern Recognition and Graphics Robotics Robotics and Automation Robots Trajectories Velocity Vision |
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| Title | A novel framework for generalizing dynamic movement primitives under kinematic constraints |
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