Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data

P. Englert, A. Paraschos, J. Peters, and M. P. Deisenroth, “Model-based imitation learning by probabilistic trajectory matching,” in IEEE International Conference on Robotics and Automation, 2013, pp. 1922–1927

1927

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.

J. J. Kuffner, “Cloud-enabled robots,” in IEEE-RAS International Conference on Humanoid Robotics, Nashville, TN, 2010

2010

Earlier work this paper cites.

K. Mülling, J. Kober, O. Kroemer, and J. Peters, “Learning to select and generalize striking movements in robot table tennis,” The International Journal of Robotics Research, vol. 32, no. 3, pp. 263-279, 2013

2013

Earlier work this paper cites.

Ben Kehoe, Akihiro Matsukawa, Sal Candido, James Kuffner, and Ken Goldberg. “Cloud-based robot grasping with the google object recognition engine,” In IEEE International Conference on Robotics and Automation (ICRA), pp. 4263–4270, 2013

2013

Earlier work this paper cites.

Haiyan Wu, Lei Lou, Chih-Chung Chen, Sandra Hirche, and Kolja Kuhnlenz. “Cloud-based networked visual servo control,” in IEEE Transactions on Industrial Electronics, Vol. 60, No. 2, pp. 554 - 566, 2013

2013

Earlier work this paper cites.

Luis Riazuelo, Javier Civera, et al. “C2tam: A cloud framework for cooperative tracking and mapping. Robotics and Autonomous Systems,” vol. 62, no.4, pp.401– 413, 2014

2014

Earlier work this paper cites.

A. Giusti et al., “A machine learning approach to visual perception of forest trails for mobile robots,” IEEE Robotics and Automation Letters (RA-L), vol. 1, no. 2, pp. 661–667, 2015

2015

Earlier work this paper cites.

G. Mohanarajah, V. Usenko, M. Singh, et al., “Cloud-based collaborative 3D mapping in real-time with low-cost robots,” IEEE Trans. Autom. Sci. Eng., vol. 12, no. 2, pp. 423–431, 2015

2015

Earlier work this paper cites.

Javier Salmeron-Garcı, Pablo Inigo-Blasco, et al. “A tradeoff analysis of a cloud-based robot navigation assistant using stereo image processing,” IEEE Transactions on Automation Science and Engineering, Vol. 12, No. 2, pp. 444–454, 2015

2015

Cited alongside, same era.

Komei Sugiura and Koji Zettsu. “Rospeex: A cloud robotics platform for human-robot spoken dialogues,” in IEEE International Conference on Intelligent Robots and Systems (IROS), pp.6155-6160, 2015

2015

Cited alongside, same era.

Wang L , Liu M , Meng Q H. “Real-Time Multisensor Data Retrieval for Cloud Robotic Systems,” IEEE Transactions on Automation Science and Engineering, vol. 12, no. 2, pp. 507-518, 2015

2015

Cited alongside, same era.

M. Bojarski et al., “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316, 2016

Original

2016

Cited alongside, same era.

Jonatan S. Dyrstad, et al. “Teaching a Robot to Grasp Real Fish by Imitation Learning from a Human Supervisor in Virtual Reality,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2018: 7185-7192

2018

Later among the works it cites.

T. Yu et al., “One-shot imitation from observing humans via domain-adaptive meta-learning,” arXiv Prepr. arXiv1802.01557, 2018

2018

Later among the works it cites.

M. Nasr, R. Shokri, and A. Houmansadr, “Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks,” in IEEE Symposium on Security and Privacy, 2018, pp. 1-15

2018

Later among the works it cites.

A. Hard et al., “Federated Learning for Mobile Keyboard Prediction,” arXiv preprint arXiv:1811.03604, 2018

Original

2018

Later among the works it cites.