A Learning Framework for High Precision Industrial Assembly

R. J. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning,” Machine learning , vol. 8, no. 3-4, pp. 229–256, 1992

1992

Earlier work this paper cites.

Y. Tassa, T. Erez, and E. Todorov, “Synthesis and stabilization of complex behaviors through online trajectory optimization,” in Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on . IEEE, 2012, pp. 4906–4913

2012

Earlier work this paper cites.

E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on . IEEE, 2012, pp. 5026–5033

2012

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.

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 , vol. 518, no. 7540, p. 529, 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,” arXiv preprint arXiv:1509.02971 , 2015

Original

2015

Cited alongside, same era.

J. W. Kalat, Introduction to psychology . Nelson Education, 2016

2016

Cited alongside, same era.

T. Tang, H.-C. Lin, Y. Zhao, Y. Fan, W. Chen, and M. Tomizuka, “Teach industrial robots peg-hole-insertion by human demonstration,” in Advanced Intelligent Mechatronics (AIM), 2016 IEEE International Conference on . IEEE, 2016, pp. 488–494

2016

Cited alongside, same era.

S. Levine, C. Finn, T. Darrell, and P. Abbeel, “End-to-end training of deep visuomotor policies,” The Journal of Machine Learning Research , vol. 17, no. 1, pp. 1334–1373, 2016

2016

Cited alongside, same era.

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

M. Vecerík, T. Hester, J. Scholz, F. Wang, O. Pietquin, B. Piot, N. Heess, T. Rothörl, T. Lampe, and M. A. Riedmiller, “Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards,” CoRR, abs/1707.08817 , 2017

Original

2017

Later among the works it cites.

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

2017

Later among the works it cites.

S. Fujimoto, H. van Hoof, and D. Meger, “Addressing function approximation error in actor-critic methods,” arXiv preprint arXiv:1802.09477 , 2018

Original

2018

Closest in time.

T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” arXiv preprint arXiv:1801.01290 , 2018

Original

2018

Closest in time.