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Randomization is currently a widely used approach in Sim2Real transfer for data-driven learning algorithms in robotics.
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1995
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N. Jakobi, “Running Across the Reality Gap: Octopod Locomotion Evolved in a Minimal Simulation,” in European Workshop on Evolutionary Robotics (EvoRobots) , ser. LNCS, vol. 1468. Paris, France: Springer, 1998, pp. 39–58
1998
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J. C. Zagal, J. Ruiz-del-Solar, and P. Vallejos, “Back to Reality: Crossing the Reality Gap in Evolutionary Robotics,” IFAC Proceedings Volumes , vol. 37, no. 8, pp. 834–839, 2004
2004
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E. Todorov, T. Erez, and Y. Tassa, “MuJoCo: A physics engine for model-based control,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Vilamoura-Algarve, Portugal, 2012, pp. 5026–5033
2012
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V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing Atari with Deep Reinforcement Learning,” in NIPS: Deep Learning Workshop , 2013
2013
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S. Koos, J.-B. Mouret, and S. Doncieux, “The Transferability Approach: Crossing the Reality Gap in Evolutionary Robotics,” IEEE Transactions on Evolutionary Computation , vol. 17, no. 1, pp. 122–145, 2013
2013
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C. Stahlhut, N. Navarro-Guerrero, C. Weber, and S. Wermter, “Interaction in Reinforcement Learning Reduces the Need for Finely Tuned Hyperparameters in Complex Tasks,” Kognitive Systeme , vol. 3, no. 2, 2015
2015
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D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the Game of Go with Deep Neural Networks and Tree Search,” Nature , vol. 529, no. 7587, pp. 484–489, 2016
2016
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2016
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S. James and E. Johns, “3D Simulation for Robot Arm Control with Deep Q-Learning,” in NIPS Workshop: Deep Learning for Action and Interaction , Barcelona, Spain, 2016
2016
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E. Falotico, L. Vannucci, A. Ambrosano, U. Albanese, S. Ulbrich, J. C. Vasquez Tieck, G. Hinkel, J. Kaiser, I. Peric, O. Denninger, N. Cauli, M. Kirtay, A. Roennau, G. Klinker, A. Von Arnim, L. Guyot, D. Peppicelli, P. Martínez-Cañada, E. Ros, P. Maier, S. Weber, M. Huber, D. Plecher, F. Röhrbein, S. Deser, A. Roitberg, P. van der Smagt, R. Dillman, P. Levi, C. Laschi, A. C. Knoll, and M.-O. Gewaltig, “Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform,” Frontiers in Neurorobotics , vol. 11, no. 2, 2017
2017
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J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Vancouver, BC, Canada, 2017, pp. 23–30
2017
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A. A. Rusu, M. Večerík, T. Rothörl, N. Heess, R. Pascanu, and R. Hadsell, “Sim-to-Real Robot Learning from Pixels with Progressive Nets,” in Annual Conference on Robot Learning (CoRL) , vol. 78. Mountain View, CA, USA: PMLR, 2017, pp. 262–270
2017
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N. Navarro-Guerrero, R. Lowe, and S. Wermter, “Improving Robot Motor Learning with Negatively Valenced Reinforcement Signals,” Frontiers in Neurorobotics , vol. 11, no. 10, 2017
2017
Cited alongside, same era.
C. Hennersperger, B. Fuerst, S. Virga, O. Zettinig, B. Frisch, T. Neff, and N. Navab, “Towards MRI-Based Autonomous Robotic Us Acquisitions: A First Feasibility Study,” IEEE Transactions on Medical Imaging , vol. 36, no. 2, pp. 538–548, 2017
2017
Cited alongside, same era.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal Policy Optimization Algorithms,” Tech. Rep. arXiv: 1707.06347, 2017
2017
Cited alongside, same era.
S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen, “Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection,” The International Journal of Robotics Research , vol. 37, no. 4-5, pp. 421–436, 2018
2018
Cited alongside, same era.
M. Malmir, J. Josifovski, N. Klarmann, and A. Knoll, “Robust Sim2Real Transfer by Learning Inverse Dynamics of Simulated Systems,” in 2nd R:SS Workshop on Closing the Reality Gap in Sim2Real Transfer for Robotics , Corvallis, OR, USA, 2020, p. 3
2020
Later among the works it cites.
W. Zhao, J. P. Queralta, and T. Westerlund, “Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: A Survey,” in IEEE Symposium Series on Computational Intelligence (SSCI) , Canberra, ACT, Australia, 2020, pp. 737–744
2020
Later among the works it cites.
M. Kaspar, J. D. Muñoz Osorio, and J. Bock, “Sim2Real Transfer for Reinforcement Learning Without Dynamics Randomization,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Las Vegas, NV, USA, 2020, pp. 4383–4388
2020
Later among the works it cites.
A. Kadian, J. Truong, A. Gokaslan, A. Clegg, E. Wijmans, S. Lee, M. Savva, S. Chernova, and D. Batra, “Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance?” in 2nd R:SS Workshop on Closing the Reality Gap in Sim2Real Transfer for Robotics , Corvallis, OR, USA, 2020, p. 3
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J. Josifovski, M. Kerzel, C. Pregizer, L. Posniak, and S. Wermter, “Object Detection and Pose Estimation Based on Convolutional Neural Networks Trained with Synthetic Data,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Madrid, Spain, 2018, pp. 6269–6276
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,” in IEEE International Conference on Robotics and Automation (ICRA) , Brisbane, QLD, Australia, 2018, pp. 3803–3810
2018
Cited alongside, same era.
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 Robotics: Science and Systems (R:SS) , vol. 14, Pittsburgh, PA, USA, 2018
2018
Cited alongside, same era.
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,” 2018
2018
Cited alongside, same era.
A. Raffin, “Rl baselines zoo,” https://github.com/araffin/rl-baselines-zoo , 2018
2018
Cited alongside, same era.
2019
Cited alongside, same era.
S. James, P. Wohlhart, M. Kalakrishnan, D. Kalashnikov, A. Irpan, J. Ibarz, S. Levine, R. Hadsell, and K. Bousmalis, “Sim-to-Real Via Sim-to-Sim: Data-Efficient Robotic Grasping Via Randomized-to-Canonical Adaptation Networks,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Long Beach, CA, USA, 2019, pp. 12 619–12 629
2019
Cited alongside, same era.
B. Baker, I. Kanitscheider, T. Markov, Y. Wu, G. Powell, B. McGrew, and I. Mordatch, “Emergent Tool Use from Multi-Agent Autocurricula,” in International Conference on Learning Representations (ICLR) , ser. Eight, Virtual from Addis Ababa, Ethiopia, 2020
2020
Cited alongside, same era.
2020
Later among the works it cites.
J. Josifovski, M. Malmir, N. Klarmann, and A. Knoll, “Continual Learning on Incremental Simulations for Real-World Robotic Manipulation Tasks,” in 2nd R:SS Workshop on Closing the Reality Gap in Sim2Real Transfer for Robotics , Corvallis, OR, USA, 2020, p. 3
2020
Later among the works it cites.
“Unity 3d,” https://unity.com/ , accessed: 2020-6-26
2020
Later among the works it cites.
“Kuka lbr-iiwa,” https://www.kuka.com/products/robot-systems/industrial-robots/lbr-iiwa , accessed: 2020-6-26
2020
Later among the works it cites.
V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, and G. State, “Isaac Gym: High Performance GPU Based Physics Simulation for Robot Learning,” in Conference on Neural Information Processing Systems (NeurIPS) , ser. Datasets and Benchmarks Track, Virtual Event, 2021
2021
Later among the works it cites.
2022
Closest in time.
“Open ai reacher-v2 environment,” https://gym.openai.com/envs/Reacher-v2/ , accessed: 2022-2-23
2022
Closest in time.
“Ros industrial,” https://github.com/ros-industrial/kuka_experimental , accessed: 2022-2-23
2022
Closest in time.
“Unity robotics hub,” https://github.com/Unity-Technologies/Unity-Robotics-Hub/blob/main/tutorials/urdf_importer/urdf_appendix.md , accessed: 2022-2-23
2022
Closest in time.