Fetching the paper…
Reading the bibliography…
One approach to Imitation Learning is Behavior Cloning, in which a robot observes a supervisor and infers a control policy.
The white-noise method in system identification
P. Z. Marmarelis and V. Z. Marmarelis · 1978
Earlier work this paper cites.
Persistence of excitation in linear systems
M. Green and J. B. Moore · 1986
Earlier work this paper cites.
Alvinn: An autonomous land vehicle in a neural network
D. A. Pomerleau · 1989
Earlier work this paper cites.
Persistency of excitation and (lack of) robustness in adaptive systems
W. A. Sethares and C. Johnson Jr · 1989
Earlier work this paper cites.
Reinforcement learning: An introduction
R. S. Sutton and A. G. Barto · 1998
Earlier work this paper cites.
Shrinkage estimators for covariance matrices
M. J. Daniels and R. E. Kass · 2001
Earlier work this paper cites.
Learning with kernels: Support vector machines, regularization, optimization, and beyond
B. Schölkopf and A. J. Smola · 2002
Earlier work this paper cites.
Efficient reductions for imitation learning
S. Ross and D. Bagnell · 2010
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
S. Ross, G. J. Gordon, and J. A. Bagnell · 2010
Cited alongside, same era.
Adaptive control: stability, convergence and robustness
S. Sastry and M. Bodson · 2011
Cited alongside, same era.
Mujoco: A physics engine for model-based control
E. Todorov, T. Erez, and Y. Tassa · 2012
Cited alongside, same era.
An investigation of imitation learning algorithms for structured prediction
A. Vlachos · 2013
Cited alongside, same era.
Deep learning for real-time atari game play using offline monte-carlo tree search planning
X. Guo, S. Singh, H. Lee, R. L. Lewis, and X. Wang · 2014
Cited alongside, same era.
Shannon entropy: a rigorous notion at the crossroads between probability, information theory, dynamical systems and statistical physics
End to end learning for self-driving cars
M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, et al · 2016
Later among the works it cites.
Lessons from the amazon picking challenge
N. Correll, K. E. Bekris, D. Berenson, O. Brock, A. Causo, K. Hauser, K. Okada, A. Rodriguez, J. M. Romano, and P. R. Wurman · 2016
Later among the works it cites.
Generative adversarial imitation learning
J. Ho and S. Ermon · 2016
Later among the works it cites.
Robot grasping in clutter: Using a hierarchy of supervisors for learning from demonstrations
M. Laskey, J. Lee, C. Chuck, D. Gealy, W. Hsieh, F. T. Pokorny, A. D. Dragan, and K. Goldberg · 2016
Later among the works it cites.
Learning from demonstrations through the use of non-rigid registration
J. Schulman, J. Ho, C. Lee, and P. Abbeel · 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…
A. Lesne · 2014
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
Trust region policy optimization
J. Schulman, S. Levine, P. Abbeel, M. Jordan, and P. Moritz · 2015
Cited alongside, same era.
Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations
M. Laskey, C. Chuck, J. Lee, J. Mahler, S. Krishnan, K. Jamieson, A. Dragan, and K. Goldberg
Cited in the paper.
Query-efficient imitation learning for end-to-end autonomous driving
J. Zhang and K. Cho · 2016
Later among the works it cites.
Deeply aggrevated: Differentiable imitation learning for sequential prediction
W. Sun, A. Venkatraman, G. J. Gordon, B. Boots, and J. A. Bagnell · 2017
Closest in time.