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Movement Primitives (MPs) are a well-established method for representing and generating modular robot trajectories.
S. Schaal, “Dynamic movement primitives-a framework for motor control in humans and humanoid robotics,” in Adaptive motion of animals and machines . Springer, 2006, pp. 261–280
2006
Earlier work this paper cites.
A. Ude, A. Gams, T. Asfour, and J. Morimoto, “Task-specific generalization of discrete and periodic dynamic movement primitives,” IEEE Transactions on Robotics , vol. 26, no. 5, pp. 800–815, 2010
2010
Earlier work this paper cites.
A. Paraschos, C. Daniel, J. R. Peters, and G. Neumann, “Probabilistic movement primitives,” Advances in neural information processing systems , vol. 26, 2013
2013
Earlier work this paper cites.
H. B. Amor, G. Neumann, S. Kamthe, O. Kroemer, and J. Peters, “Interaction primitives for human-robot cooperation tasks,” in 2014 IEEE international conference on robotics and automation (ICRA) . IEEE, 2014, pp. 2831–2837
2014
Earlier work this paper cites.
M. Do, J. Schill, J. Ernesti, and T. Asfour, “Learn to wipe: A case study of structural bootstrapping from sensorimotor experience,” in 2014 IEEE International Conference on Robotics and Automation (ICRA) , 2014, pp. 1858–1864
2014
Earlier work this paper cites.
F. Steinmetz, A. Montebelli, and V. Kyrki, “Simultaneous kinesthetic teaching of positional and force requirements for sequential in-contact tasks,” in 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids) , 2015, pp. 202–209
2015
Earlier work this paper cites.
F. J. Abu-Dakka, B. Nemec, J. A. Jørgensen, T. R. Savarimuthu, N. Krüger, and A. Ude, “Adaptation of manipulation skills in physical contact with the environment to reference force profiles,” Autonomous Robots , vol. 39, pp. 199–217, 2015
2015
Earlier work this paper cites.
A. Montebelli, F. Steinmetz, and V. Kyrki, “On handing down our tools to robots: Single-phase kinesthetic teaching for dynamic in-contact tasks,” in 2015 IEEE International Conference on Robotics and Automation (ICRA) , 2015, pp. 5628–5634
2015
Earlier work this paper cites.
R. Lioutikov, G. Neumann, G. Maeda, and J. Peters, “Probabilistic segmentation applied to an assembly task,” in 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids) , 2015, pp. 533–540
2015
Earlier work this paper cites.
Y. Zhou, M. Do, and T. Asfour, “Learning and force adaptation for interactive actions,” in 2016 IEEE-RAS 16th international conference on humanoid robots (humanoids) . IEEE, 2016, pp. 1129–1134
2016
Earlier work this paper cites.
R. Lioutikov, O. Kroemer, G. Maeda, and J. Peters, “Learning manipulation by sequencing motor primitives with a two-armed robot,” in Intelligent Autonomous Systems 13 , E. Menegatti, N. Michael, K. Berns, and H. Yamaguchi, Eds. Cham: Springer International Publishing, 2016, pp. 1601–1611
2016
Earlier work this paper cites.
S. Manschitz, M. Gienger, J. Kober, and J. Peters, “Probabilistic decomposition of sequential force interaction tasks into movement primitives,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2016, pp. 3920–3927
2016
Earlier work this paper cites.
M. Deniša, A. Gams, A. Ude, and T. Petrič, “Learning compliant movement primitives through demonstration and statistical generalization,” IEEE/ASME Transactions on Mechatronics , vol. 21, no. 5, pp. 2581–2594, 2016
2016
Cited alongside, same era.
A. Paraschos, R. Lioutikov, J. Peters, and G. Neumann, “Probabilistic prioritization of movement primitives,” IEEE Robotics and Automation Letters , vol. 2, no. 4, pp. 2294–2301, 2017
2017
Cited alongside, same era.
T. Petrič, A. Gams, L. Colasanto, A. J. Ijspeert, and A. Ude, “Accelerated sensorimotor learning of compliant movement primitives,” IEEE Transactions on Robotics , vol. 34, no. 6, pp. 1636–1642, 2018
2018
Cited alongside, same era.
Y. Zhou, J. Gao, and T. Asfour, “Learning via-point movement primitives with inter-and extrapolation capabilities,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 4301–4308
2019
Cited alongside, same era.
Y. Lai, G. Paul, Y. Cui, and T. Matsubara, “User intent estimation during robot learning using physical human robot interaction primitives,” Autonomous Robots , vol. 46, no. 2, pp. 421–436, 2022
2022
Later among the works it cites.
F. Otto, O. Celik, H. Zhou, H. Ziesche, V. A. Ngo, and G. Neumann, “Deep black-box reinforcement learning with movement primitives,” in Proceedings of The 6th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, K. Liu, D. Kulic, and J. Ichnowski, Eds., vol. 205. PMLR, 14–18 Dec 2023, pp. 1244–1265. [Online]. Available: https://proceedings.mlr.press/v205/otto23a.html
2023
Later among the works it cites.
G. Franzese, L. d. S. Rosa, T. Verburg, L. Peternel, and J. Kober, “Interactive imitation learning of bimanual movement primitives,” IEEE/ASME Transactions on Mechatronics , pp. 1–13, 2023
2023
Later among the works it cites.
M. X. Li, O. Celik, P. Becker, D. Blessing, R. Lioutikov, and G. Neumann, “Curriculum-based imitation of versatile skills,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 2951–2957
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S. Manschitz, M. Gienger, J. Kober, and J. Peters, “Learning sequential force interaction skills,” Robotics , vol. 9, no. 2, 2020. [Online]. Available: https://www.mdpi.com/2218-6581/9/2/45
2020
Cited alongside, same era.
R. Lioutikov, G. Maeda, F. Veiga, K. Kersting, and J. Peters, “Learning attribute grammars for movement primitive sequencing,” The International Journal of Robotics Research , vol. 39, no. 1, pp. 21–38, 2020
2020
Cited alongside, same era.
K. Kimble, K. Van Wyk, J. Falco, E. Messina, Y. Sun, M. Shibata, W. Uemura, and Y. Yokokohji, “Benchmarking protocols for evaluating small parts robotic assembly systems,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 883–889, 2020
2020
Cited alongside, same era.
Y. Cohen, O. Bar-Shira, and S. Berman, “Motion adaptation based on learning the manifold of task and dynamic movement primitive parameters,” Robotica , vol. 39, no. 7, p. 1299–1315, 2021
2021
Cited alongside, same era.
Y. Wang, C. C. Beltran-Hernandez, W. Wan, and K. Harada, “Robotic imitation of human assembly skills using hybrid trajectory and force learning,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2021, pp. 11 278–11 284
2021
Cited alongside, same era.
A. X. Lee, C. M. Devin, Y. Zhou, T. Lampe, K. Bousmalis, J. T. Springenberg, A. Byravan, A. Abdolmaleki, N. Gileadi, D. Khosid, et al. , “Beyond pick-and-place: Tackling robotic stacking of diverse shapes,” in 5th Annual Conference on Robot Learning , 2021
2021
Cited alongside, same era.
Y. Lin, A. S. Wang, G. Sutanto, A. Rai, and F. Meier, “Polymetis,” https://facebookresearch.github.io/fairo/polymetis/ , 2021
2021
Cited alongside, same era.
S. Dou, J. Xiao, W. Zhao, H. Yuan, and H. Liu, “A robot skill learning framework based on compliant movement primitives,” Journal of Intelligent & Robotic Systems , vol. 104, no. 3, p. 53, 2022
2022
Cited alongside, same era.
2023
Later among the works it cites.
G. Li, Z. Jin, M. Volpp, F. Otto, R. Lioutikov, and G. Neumann, “Prodmp: A unified perspective on dynamic and probabilistic movement primitives,” IEEE Robotics and Automation Letters , vol. 8, no. 4, pp. 2325–2332, 2023
2023
Later among the works it cites.
Y. Wang, C. Chen, F. Peng, Z. Zheng, Z. Gao, R. Yan, and X. Tang, “Al-promp: Force-relevant skills learning and generalization method for robotic polishing,” Robotics and Computer-Integrated Manufacturing , vol. 82, p. 102538, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0736584523000145
2023
Later among the works it cites.
M. Mittal, C. Yu, Q. Yu, J. Liu, N. Rudin, D. Hoeller, J. L. Yuan, R. Singh, Y. Guo, H. Mazhar, A. Mandlekar, B. Babich, G. State, M. Hutter, and A. Garg, “Orbit: A unified simulation framework for interactive robot learning environments,” IEEE Robotics and Automation Letters , vol. 8, no. 6, pp. 3740–3747, 2023
2023
Later among the works it cites.
M. Heo, Y. Lee, D. Lee, and J. J. Lim, “Furniturebench: Reproducible real-world benchmark for long-horizon complex manipulation,” in Robotics: Science and Systems , 2023
2023
Later among the works it cites.
P. M. Scheikl, N. Schreiber, C. Haas, N. Freymuth, G. Neumann, R. Lioutikov, and F. Mathis-Ullrich, “Movement primitive diffusion: Learning gentle robotic manipulation of deformable objects,” IEEE Robotics and Automation Letters , 2024
2024
Closest in time.
D. Blessing, O. Celik, X. Jia, M. Reuss, M. Li, R. Lioutikov, and G. Neumann, “Information maximizing curriculum: A curriculum-based approach for learning versatile skills,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
B. Abbatematteo, E. Rosen, S. Thompson, T. Akbulut, S. Rammohan, and G. Konidaris, “Composable interaction primitives: A structured policy class for efficiently learning sustained-contact manipulation skills,” IEEE International Conference on Robotics and Automation , 2024
2024
Closest in time.