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It is desirable for future robots to quickly learn new tasks and adapt learned skills to constantly changing environments.
R. S. Sutton, D. A. McAllester, S. P. Singh, and Y. Mansour, “Policy gradient methods for reinforcement learning with function approximation,” in Advances in Neural Information Processing Systems (NIPS) , 1999, pp. 1057–1063
1999
Earlier work this paper cites.
A. Albu-Schaffer, C. Ott, U. Frese, and G. Hirzinger, “Cartesian impedance control of redundant robots: recent results with the dlr-light-weight-arms,” in 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422) , vol. 3, 2003, pp. 3704–3709 vol.3
2003
Earlier work this paper cites.
S. Calinon, F. Guenter, and A. Billard, “On learning, representing, and generalizing a task in a humanoid robot,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) , vol. 37, no. 2, pp. 286–298, 2007
2007
Earlier work this paper cites.
A. Billard, S. Calinon, R. Dillmann, and S. Schaal, “Survey: Robot programming by demonstration,” Springrer, Tech. Rep., 2008
2008
Earlier work this paper cites.
J. Peters and S. Schaal, “Natural actor-critic,” Neurocomput. , vol. 71, no. 7–9, p. 1180–1190, Mar. 2008
2008
Earlier work this paper cites.
B. Kim, J. Park, S. Park, and S. Kang, “Impedance learning for robotic contact tasks using natural actor-critic algorithm,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) , vol. 40, no. 2, pp. 433–443, 2009
2009
Earlier work this paper cites.
J. Kober and J. Peters, “Policy search for motor primitives in robotics,” Machine Learning , vol. 84, no. 1-2, pp. 171–203, 2011
2011
Earlier work this paper cites.
T. Degris, M. White, and R. S. Sutton, “Off-policy actor-critic,” in Proceedings of the 29th International Coference on International Conference on Machine Learning , ser. ICML’12. Madison, WI, USA: Omnipress, 2012, p. 179–186
2012
Earlier work this paper cites.
S. M. Khansari-Zadeh and A. Billard, “A dynamical system approach to realtime obstacle avoidance,” Autonomous Robots , vol. 32, no. 4, pp. 433–454, 2012, the final publication is available at www.springerlink.com. [Online]. Available: http://infoscience.epfl.ch/record/174759
2012
Earlier work this paper cites.
A. Paraschos, C. Daniel, J. R. Peters, and G. Neumann, “Probabilistic movement primitives,” in Advances in Neural Information Processing Systems , C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, Eds., vol. 26. Curran Associates, Inc., 2013
2013
Earlier work this paper cites.
A. J. Ijspeert, J. Nakanishi, H. Hoffmann, P. Pastor, and S. Schaal, “Dynamical movement primitives: Learning attractor models for motor behaviors,” Neural Computation , vol. 25, no. 2, pp. 328–373, 2013
2013
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-Encoding Variational Bayes,” in 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings , 2014
2014
Earlier work this paper cites.
M. Conforti, G. Cornuejols, and G. Zambelli, Integer Programming . Springer Publishing Company, Incorporated, 2014
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Paraschos, C. Daniel, J. Peters, and G. Neumann, “Using probabilistic movement primitives in robotics,” Autonomous Robots , vol. 42, no. 3, pp. 529–551, 2018
2018
Cited alongside, same era.
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in Proceedings of International Conference on Machine Learning (ICML) , vol. 80, 2018, pp. 1856–1865
2018
Cited alongside, same era.
S. Fujimoto, H. van Hoof, and D. Meger, “Addressing function approximation error in actor-critic methods,” in Proceedings of the 35th International Conference on Machine Learning , vol. 80, 2018, pp. 1587–1596
2018
Cited alongside, same era.
T. Johannink, S. Bahl, A. Nair, J. Luo, A. Kumar, M. Loskyll, J. A. Ojea, E. Solowjow, and S. Levine, “Residual reinforcement learning for robot control,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 6023–6029
2019
Cited alongside, same era.
J. Urain, M. Ginesi, D. Tateo, and J. Peters, “Imitationflow: Learning deep stable stochastic dynamic systems by normalizing flows,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2020. [Online]. Available: https://www.ias.informatik.tu-darmstadt.de/uploads/Team/JulenUrainDeJesus/2020iflowurain.pdf
2020
Later among the works it cites.
O. Kroemer, S. Niekum, and G. D. Konidaris, “A review of robot learning for manipulation: Challenges, representations, and algorithms,” Journal of machine learning research , vol. 22, no. 30, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
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R. Martín-Martín, M. A. Lee, R. Gardner, S. Savarese, J. Bohg, and A. Garg, “Variable impedance control in end-effector space: An action space for reinforcement learning in contact-rich tasks,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 1010–1017
2019
Cited alongside, same era.
Y. Ding, C. Florensa, P. Abbeel, and M. Phielipp, “Goal-conditioned imitation learning,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
Y. Huang, L. Rozo, J. Silvério, and D. Caldwell, “Kernelized movement primitives,” The International Journal of Robotics Research , vol. 38, pp. 833–852, 05 2019
2019
Cited alongside, same era.
D. Koert, J. Pajarinen, A. Schotschneider, S. Trick, C. Rothkopf, and J. Peters, “Learning intention aware online adaptation of movement primitives,” IEEE Robotics and Automation Letters , vol. 4, no. 4, pp. 3719–3726, 2019
2019
Cited alongside, same era.
G. Schoettler, A. Nair, J. Luo, S. Bahl, J. A. Ojea, E. Solowjow, and S. Levine, “Deep reinforcement learning for industrial insertion tasks with visual inputs and natural rewards,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 5548–5555
2020
Cited alongside, same era.
S. Gomez-Gonzalez, G. Neumann, B. Schölkopf, and J. Peters, “Adaptation and robust learning of probabilistic movement primitives,” IEEE Transactions on Robotics , vol. 36, no. 2, pp. 366–379, Mar. 2020
2020
Cited alongside, same era.
C. C. Beltran-Hernandez, D. Petit, I. G. Ramirez-Alpizar, T. Nishi, S. Kikuchi, T. Matsubara, and K. Harada, “Learning force control for contact-rich manipulation tasks with rigid position-controlled robots,” IEEE Robotics and Automation Letters , vol. 5, no. 4, pp. 5709–5716, 2020
2020
Cited alongside, same era.
C. C. Beltran-Hernandez, D. Petit, I. G. Ramirez-Alpizar, and K. Harada, “Variable compliance control for robotic peg-in-hole assembly: A deep-reinforcement-learning approach,” Applied Sciences , vol. 10, no. 19, p. 6923, 2020
2020
Cited alongside, same era.
2021
Later among the works it cites.
P. Kulkarni, J. Kober, R. Babuška, and C. Della Santina, “Learning assembly tasks in a few minutes by combining impedance control and residual recurrent reinforcement learning,” Advanced Intelligent Systems , p. 2100095, 2021
2021
Later among the works it cites.
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) . IEEE, 2021, pp. 11 278–11 284
2021
Later among the works it cites.
Y. Wang, C. C. Beltran-Hernandez, W. Wan, and K. Harada, “Hybrid trajectory and force learning of complex assembly tasks: A combined learning framework,” IEEE Access , vol. 9, pp. 60 175–60 186, 2021
2021
Later among the works it cites.
A. Ranjbar, N. A. Vien, H. Ziesche, J. Boedecker, and G. Neumann, “Residual feedback learning for contact-rich manipulation tasks with uncertainty,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 2383–2390
2021
Later among the works it cites.
Y. Shi, Z. Chen, Y. Wu, D. Henkel, S. Riedel, H. Liu, Q. Feng, and J. Zhang, “Combining learning from demonstration with learning by exploration to facilitate contact-rich tasks,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 1062–1069
2021
Later among the works it cites.
O. Spector and D. Di Castro, “Insertionnet-a scalable solution for insertion,” IEEE Robotics and Automation Letters , vol. 6, no. 3, pp. 5509–5516, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
“Ubongo 3d,” May 2021. [Online]. Available: https://www.kosmosgames.co.uk/games/ubongo-3d/
2021
Later among the works it cites.
L. Rozo* and V. Dave*, “Orientation probabilistic movement primitives on riemannian manifolds,” in Conference on Robot Learning , vol. 5, 2021, p. 11. [Online]. Available: https://cps.unileoben.ac.at/wp/orientation_probabilistic_move.pdf,ArticleFile
2021
Later among the works it cites.
T. B. Davchev, K. S. Luck, M. Burke, F. Meier, S. Schaal, and S. Ramamoorthy, “Residual learning from demonstration: Adapting dmps for contact-rich manipulation,” IEEE Robotics and Automation Letters , 2022
2022
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N. J. Cho, S. H. Lee, J. B. Kim, and I. H. Suh, “Learning, improving, and generalizing motor skills for the peg-in-hole tasks based on imitation learning and self-learning,” Applied Sciences , vol. 10, no. 8, 2020. [Online]. Available: https://www.mdpi.com/2076-3417/10/8/2719
2076
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