HG-DAgger: Interactive Imitation Learning with Human Experts

A. Bhattacharyya, “On a measure of divergence between two statistical populations defined by their probability distributions,” Bulletin of the Calcutta Mathematical Society , vol. 35, pp. 99–109, 1943

1943

Earlier work this paper cites.

D. A. Pomerleau, “ALVINN: An autonomous land vehicle in a neural network,” in Advances in Neural Information Processing Systems , 1989, pp. 305–313

1989

Earlier work this paper cites.

W. S. Kim, B. Hannaford, and A. Fejczy, “Force-reflection and shared compliant control in operating telemanipulators with time delay,” IEEE Transactions on Robotics and Automation , vol. 8, no. 2, pp. 176–185, 1992

1992

Earlier work this paper cites.

D. T. McRuer, “Pilot-induced oscillations and human dynamic behavior,” NASA Technical Report , 1995

1995

Earlier work this paper cites.

D. McRuer, Aviation safety and pilot control: Understanding and preventing unfavorable pilot-vehicle interactions . National Academies Press, 1997

1997

Earlier work this paper cites.

M. S. Branicky, “Multiple Lyapunov functions and other analysis tools for switched and hybrid systems,” IEEE Transactions on Automatic Control , vol. 43, no. 4, pp. 475–482, 1998

1998

Earlier work this paper cites.

B. Price and C. Boutilier, “Accelerating reinforcement learning through implicit imitation,” Journal of Artificial Intelligence Research , vol. 19, pp. 569–629, 2003

2003

Earlier work this paper cites.

S. Chernova and M. Veloso, “Interactive policy learning through confidence-based autonomy,” in Journal of Artificial Intelligence Research (JAIR) , 2009, pp. 1–25

2009

Earlier work this paper cites.

J. Kober and J. Peters, “Imitation and reinforcement learning,” IEEE Robotics Automation Magazine , vol. 17, no. 2, pp. 55–62, June 2010

2010

Cited alongside, same era.

S. Ross and D. Bagnell, “Efficient reductions for imitation learning,” in International Conference on Artificial Intelligence and Statistics , 2010, pp. 661–668

2010

Cited alongside, same era.

H. He, H. Daumé III, and J. Eisner, “Imitation learning by coaching,” in Thirty-second Conference on Neural Information Processing System , 2012

2012

Cited alongside, same era.

A. Goil, M. Derry, and B. D. Argall, “Using machine learning to blend human and robot controls for assisted wheelchair navigation,” in IEEE International Conference on Rehabilitation Robotics (ICORR) , 2013

2013

Cited alongside, same era.

B. Kim and J. Pineau, “Maximum mean discrepancy imitation learning,” in Robotics: Science and Systems , 2013

M. Laskey, S. Staszak, W. Y.-S. Hsieh, J. Mahler, et al. , “Shiv: Reducing supervisor burden in DAgger using support vectors for efficient learning from demonstrations in high dimensional state spaces,” in IEEE International Conference on Robotics and Automation (ICRA) , 2016, pp. 462–469

2016

Later among the works it cites.

M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to end learning for self-driving cars,” arXiv , no. 1604.07316, 2016

2016

Later among the works it cites.

M. Laskey, C. Chuck, J. Lee, J. Mahler, S. Krishnan, K. Jamieson, A. Dragan, and K. Goldberg, “Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations,” in IEEE International Conference on Robotics and Automation (ICRA) , 2017, pp. 358–365

2017

Later among the works it cites.

V. Govindarajan, K. Driggs-Campbell, and R. Bajcsy, “Data-driven reachability analysis for human-in-the-loop systems,” in IEEE Conference on Decision and Control (CDC) , 2017, pp. 2617–2622