Fetching the paper…
Reading the bibliography…
Diversity of environments is a key challenge that causes learned robotic controllers to fail due to the discrepancies between the training and evaluation conditions.
K. Furuta, M. Yamakita, and S. Kobayashi, “Swing-up control of inverted pendulum using pseudo-state feedback,” Journal of Systems and Control Engineering , vol. 206, pp. 263 – 269, 1992
1992
Earlier work this paper cites.
O. Nerrand, P. Roussel-Ragot, D. Urbani, L. Personnaz, and G. Dreyfus, “Training recurrent neural networks: Why and how? an illustration in dynamical process modeling,” IEEE Transactions on Neural Networks , vol. 5, no. 2, pp. 178–184, 1994
1994
Earlier work this paper cites.
H. Drucker, C. J. Burges, L. Kaufman, A. J. Smola, and V. Vapnik, “Support vector regression machines,” in Advances in neural information processing systems , 1997, pp. 155–161
1997
Earlier work this paper cites.
R. J. Frank, N. Davey, and S. P. Hunt, “Time series prediction and neural networks,” Journal of intelligent and robotic systems , vol. 31, no. 1-3, pp. 91–103, 2001
2001
Earlier work this paper cites.
C. E. Rasmussen, “Gaussian processes in machine learning,” in Advanced lectures on machine learning . Springer, 2004, pp. 63–71
2004
Earlier work this paper cites.
X. Zhu, “Semi-supervised learning literature survey,” University of Wisconsin—Madison, Tech. Rep. 1530, 2005
2005
Earlier work this paper cites.
D. H. Grollman and O. C. Jenkins, “Dogged learning for robots,” in ICRA , 2007, pp. 2483–2488
2007
Earlier work this paper cites.
B. D. Argall, S. Chernova, M. Veloso, and B. Browning, “A survey of robot learning from demonstration,” Robotics and autonomous systems , vol. 57, no. 5, pp. 469–483, 2009
2009
Earlier work this paper cites.
S. Calinon, Robot programming by demonstration . EPFL Press, 2009
2009
Earlier work this paper cites.
B. Settles, “Active learning literature survey,” University of Wisconsin–Madison, Computer Sciences Technical Report 1648, 2009
2009
Earlier work this paper cites.
S. Chernova and M. Veloso, “Interactive policy learning through confidence-based autonomy,” J. Artificial Intelligence Research , vol. 34, no. 1, p. 1, 2009
2009
Earlier work this paper cites.
P. Abbeel, A. Coates, and A. Y. Ng, “Autonomous helicopter aerobatics through apprenticeship learning,” Int. J. Robotics Research , vol. 29, no. 13, pp. 1608–1639, 2010
2010
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. Knowledge Data Eng. , vol. 22, no. 10, pp. 1345–1359, 2010
2010
Earlier work this paper cites.
A. Torralba and A. A. Efros, “Unbiased look at dataset bias,” in CVPR . IEEE, 2011, pp. 1521–1528
2011
Earlier work this paper cites.
M. Deisenroth and C. E. Rasmussen, “PILCO: A model-based and data-efficient approach to policy search,” in ICML , 2011, pp. 465–472
2011
Cited alongside, same era.
S. Ross, G. J. Gordon, and J. A. Bagnell, “No-regret reductions for imitation learning and structured prediction,” in AISTATS . Citeseer, 2011
2011
Cited alongside, same era.
S. Tellex, R. A. Knepper, A. Li, T. M. Howard, D. Rus, and N. Roy, “Assembling furniture by asking for help from a human partner,” in Collaborative manipulation workshop at human–Robot interaction , 2013
2013
Cited alongside, same era.
M. S. Gashler and S. C. Ashmore, “Training deep fourier neural networks to fit time-series data,” in International Conference on Intelligent Computing . Springer, 2014, pp. 48–55
2014
Cited alongside, same era.
A. Van Den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. W. Senior, and K. Kavukcuoglu, “Wavenet: A generative model for raw audio.” in SSW , 2016, p. 125
2016
Later among the works it cites.
2016
Later among the works it cites.
2016
Later among the works it cites.
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” CoRR, Tech. Rep. 1606.01540, 2016
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2014
Cited alongside, same era.
C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra, “Weight Uncertainty in Neural Networks,” in ICML , 2015
2015
Cited alongside, same era.
D. P. Kingma, T. Salimans, and M. Welling, “Variational dropout and the local reparameterization trick,” in Advances in Neural Information Processing Systems , 2015
2015
Cited alongside, same era.
J. X. Wang, Z. Kurth-Nelson, D. Tirumala, H. Soyer, J. Z. Leibo, R. Munos, C. Blundell, D. Kumaran, and M. Botvinick, “Learning to reinforcement learn,” CoRR, Tech. Rep. 1611.05763, 2016
2016
Cited alongside, same era.
Y. Duan, J. Schulman, X. Chen, P. L. Bartlett, I. Sutskever, and P. Abbeel, “RL 2
2016
Cited alongside, same era.
Y. Gal and Z. Ghahramani, “Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,” in ICML , 2016, pp. 1050–1059
2016
Cited alongside, same era.
D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, and D. Mané, “Concrete problems in AI safety,” CoRR, Tech. Rep. 1606.06565, 2016
2016
Cited alongside, same era.
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning requires rethinking generalization,” in ICLR , 2016
2016
Cited alongside, same era.
2017
Later among the works it cites.
C. Finn, T. Yu, T. Zhang, P. Abbeel, and S. Levine, “One-shot visual imitation learning via meta-learning,” CoRR, Tech. Rep. 1709.04905, 2017
2017
Later among the works it cites.
S. Ruder, “An overview of multi-task learning in deep neural networks,” CoRR, Tech. Rep. 1706.05098, 2017
2017
Later among the works it cites.
J. C. Gamboa Higuera, D. Meger, and G. Dudek, “Adapting learned robotics behaviours through policy adjustment,” in ICRA , 2017, pp. 5837–5843
2017
Later among the works it cites.
Z. Wang and M. E. Taylor, “Improving reinforcement learning with confidence-based demonstrations,” in IJCAI , 2017
2017
Later among the works it cites.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” CoRR, Tech. Rep. 1707.06347, 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
2018
Later among the works it cites.
L. B. Godfrey and M. S. Gashler, “Neural decomposition of time-series data for effective generalization,” IEEE transactions on neural networks and learning systems , vol. 29, no. 7, pp. 2973–2985, 2018
2018
Later among the works it cites.