A Learning Approach to Robot-Agnostic Force-Guided High Precision Assembly

P. J. Werbos, “Backpropagation through time: what it does and how to do it,” Proceedings of the IEEE , vol. 78, no. 10, pp. 1550–1560, 1990

1990

Earlier work this paper cites.

S. Levine and V. Koltun, “Guided policy search,” in International Conference on Machine Learning , 2013, pp. 1–9

2013

Earlier work this paper cites.

T. Schaul, J. Quan, I. Antonoglou, and D. Silver, “Prioritized experience replay,” arXiv preprint arXiv:1511.05952 , 2015

Original

2015

Earlier work this paper cites.

N. Heess, J. J. Hunt, T. P. Lillicrap, and D. Silver, “Memory-based control with recurrent neural networks,” arXiv preprint arXiv:1512.04455 , 2015

Original

2015

Earlier work this paper cites.

V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al. , “Human-level control through deep reinforcement learning,” Nature 518, pages 529–533 , 2015

2015

Earlier work this paper cites.

M. Hausknecht and P. Stone, “Deep recurrent q-learning for partially observable mdps,” in 2015 AAAI Fall Symposium Series , 2015

2015

Earlier work this paper cites.

T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” in 6th International Conference on Learning Representations , 2016

2016

Earlier work this paper cites.

E. Coumans and Y. Bai, “Pybullet, a python module for physics simulation for games, robotics and machine learning,” GitHub repository , 2016

2016

Earlier work this paper cites.

J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347 , 2017

Original

2017

Earlier work this paper cites.

T. Inoue, G. De Magistris, A. Munawar, T. Yokoya, and R. Tachibana, “Deep reinforcement learning for high precision assembly tasks,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 819–825

2017

Cited alongside, same era.

M. Jaderberg, V. Dalibard, S. Osindero, W. M. Czarnecki, J. Donahue, A. Razavi, O. Vinyals, T. Green, I. Dunning, K. Simonyan et al. , “Population based training of neural networks,” arXiv preprint arXiv:1711.09846 , 2017

Original

2017

Cited alongside, same era.

D. Horgan, J. Quan, D. Budden, G. Barth-Maron, M. Hessel, H. Van Hasselt, and D. Silver, “Distributed prioritized experience replay,” arXiv preprint arXiv:1803.00933 , 2018

Original

2018

Cited alongside, same era.

T. Ren, Y. Dong, D. Wu, and K. Chen, “Learning-based variable compliance control for robotic assembly,” Journal of Mechanisms and Robotics , vol. 10, no. 6, p. 061008, 2018

2018

Cited alongside, same era.

OpenAI, M. Andrychowicz, B. Baker, M. Chociej, R. Józefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray, J. Schneider, S. Sidor, J. Tobin, P. Welinder, L. Weng, and W. Zaremba, “Learning dexterous in-hand manipulation,” CoRR , 2018. [Online]. Available: http://arxiv.org/abs/1808.00177

Original

2018

Later among the works it cites.

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,” arXiv preprint arXiv:1906.05841 , 2019

Original

2019

Later among the works it cites.

I. Akkaya, M. Andrychowicz, M. Chociej, M. Litwin, B. McGrew, A. Petron, A. Paino, M. Plappert, G. Powell, R. Ribas et al. , “Solving rubik’s cube with a robot hand,” arXiv preprint arXiv:1910.07113 , 2019

Original

2019

Later among the works it cites.

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 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 8943–8950