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The dominant way to control a robot manipulator uses hand-crafted differential equations leveraging some form of inverse kinematics / dynamics.
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M. A. Lee, Y. Zhu, K. Srinivasan, P. Shah, S. Savarese, L. Fei-Fei, A. Garg, and J. Bohg, “Making sense of vision and touch: Self-supervised learning of multimodal representations for contact-rich tasks,” in ICRA , 2019
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Cited alongside, same era.
C. Florensa, D. Held, M. Wulfmeier, M. Zhang, and P. Abbeel, “Reverse curriculum generation for reinforcement learning,” in CoRL , 2017
2017
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2017
Cited alongside, same era.
2018
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C.-A. Cheng, M. Mukadam, J. Issac, S. Birchfield, D. Fox, B. Boots, and N. Ratliff, “RMPflow: A computational graph for automatic motion policy generation,” in WAFR , 2018, pp. 441–457
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D. Kappler, F. Meier, J. Issac, J. Mainprice, C. G. Cifuentes, M. Wüthrich, V. Berenz, S. Schaal, N. Ratliff, and J. Bohg, “Real-time perception meets reactive motion generation,” IEEE Robotics and Automation Letters , vol. 3, no. 3, pp. 1864–1871, 2018
2018
Cited alongside, same era.
V. Pong, S. Gu, M. Dalal, and S. Levine, “Temporal difference models: Model-free deep RL for model-based control,” in CoRL , 2018
2018
Cited alongside, same era.
F. Sadeghi, A. Toshev, E. Jang, and S. Levine, “Sim2Real view invariant visual servoing by recurrent control,” in CVPR , 2018
2018
Cited alongside, same era.
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2019
Later among the works it cites.
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2019
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2019
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J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter, “Learning agile and dynamic motor skills for legged robots,” Science Robotics , vol. 4, no. 26, Jan. 2019
2019
Later among the works it cites.
L. Shao, T. Migimatsu, Q. Zhang, K. Yang, and J. Bohg, “Concept2Robot: Learning Manipulation Concepts from Instructions and Human Demonstrations,” in RSS , 2020
2020
Closest in time.
T. Migimatsu and J. Bohg, “Object-centric task and motion planning in dynamic environments,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 844–851, 2020
2020
Closest in time.
S. Luo, H. Kasaei, and L. Schomaker, “Accelerating reinforcement learning for reaching using continuous curriculum learning,” in International Joint Conference on Neural Networks (IJCNN) , 2020
2020
Closest in time.
T. Matiisen, A. Oliver, T. Cohen, and J. Schulman, “Teacher–student curriculum learning,” IEEE Transactions on Neural Networks and Learning Systems , vol. 31, no. 9, pp. 3732–3740, 2020
2020
Closest in time.
R. Portelas, C. Colas, L. Weng, K. Hofmann, and P.-Y. Oudeyer, “Automatic curriculum learning for deep RL: A short survey,” in IJCAI , 2020
2020
Closest in time.
S. Narvekar, B. Peng, M. Leonetti, J. Sinapov, M. E. Taylor, and P. Stone, “Curriculum learning for reinforcement learning domains: A framework and survey,” JMLR , vol. 21, no. 181, pp. 1–50, 2020
2020
Closest in time.
L. Shao, T. Migimatsu, and J. Bohg, “Learning to scaffold the development of robotic manipulation skills,” in ICRA , 2020
2020
Closest in time.
M. A. Lee, C. Florensa, J. Tremblay, N. Ratliff, A. Garg, F. Ramos, and D. Fox, “Guided uncertainty-aware policy optimization: Combining learning and model-based strategies for sample-efficient policy learning,” in ICRA , 2020
2020
Closest in time.