Fetching the paper…
Reading the bibliography…
Robots can learn to do complex tasks in simulation, but often, learned behaviors fail to transfer well to the real world due to simulator imperfections (the reality gap).
N. Jakobi, P. Husbands, and I. Harvey, “Noise and the reality gap: The use of simulation in evolutionary robotics,” in
1995
Earlier work this paper cites.
A. Farchy, S. Barrett, P. MacAlpine, and P. Stone, “Humanoid robots learning to walk faster: From the real world to simulation and back,” in
2013
Earlier work this paper cites.
J. Schulman, S. Levine, P. Moritz, M. I. Jordan, and P. Abbeel, “Trust region policy optimization,” 2015
2015
Earlier work this paper cites.
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” 2016
2016
Earlier work this paper cites.
J. P. Hanna and P. Stone, “Grounded action transformation for robot learning in simulation,” in
2017
Earlier work this paper cites.
D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton
2017
Earlier work this paper cites.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” 2017
2017
Earlier work this paper cites.
L. Pinto, J. Davidson, R. Sukthankar, and A. Gupta, “Robust adversarial reinforcement learning,” 2017
2017
Cited alongside, same era.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” 2017
2017
Cited alongside, same era.
J. C. G. Higuera, D. Meger, and G. Dudek, “Adapting learned robotics behaviours through policy adjustment,” in
2017
Cited alongside, same era.
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in
2018
Cited alongside, same era.
X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel, “Sim-to-real transfer of robotic control with dynamics randomization,” 2018
2018
Cited alongside, same era.
F. Golemo, A. A. Taiga, A. Courville, and P.-Y. Oudeyer, “Sim-to-real transfer with neural-augmented robot simulation,” in
2018
Later among the works it cites.
R. Sutton and A. Barto,
2018
Later among the works it cites.
A. Hill, A. Raffin, M. Ernestus, A. Gleave, A. Kanervisto, R. Traore, P. Dhariwal, C. Hesse, O. Klimov, A. Nichol, M. Plappert, A. Radford, J. Schulman, S. Sidor, and Y. Wu, “Stable baselines,” https://github.com/hill-a/stable-baselines, 2018
2018
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, J. Schneider, N. Tezak, J. Tworek, P. Welinder, L. Weng, Q. Yuan, W. Zaremba, and L. Zhang, “Solving rubik’s cube with a robot hand,” 2019
2019
Later among the works it cites.
A. Allevato, E. S. Short, M. Pryor, and A. L. Thomaz, “Tunenet: One-shot residual tuning for system identification and sim-to-real robot task transfer,” in
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Tan, T. Zhang, E. Coumans, A. Iscen, Y. Bai, D. Hafner, S. Bohez, and V. Vanhoucke, “Sim-to-real: Learning agile locomotion for quadruped robots,” in
2018
Cited alongside, same era.
2019
Later among the works it cites.
S. Desai, H. Karnan, J. P. Hanna, G. Warnell, and P. Stone, “Stochastic grounded action transformation for robot learning in simulation,” in
2020
Closest in time.