Learning to See before Learning to Act: Visual Pre-training for Manipulation

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 8, no. 6, pp. 679–698, Jun. 1986. [Online]. Available: https://doi.org/10.1109/TPAMI.1986.4767851

1986

Earlier work this paper cites.

C. Harris and M. Stephens, “A combined corner and edge detector,” in In Proc. of Fourth Alvey Vision Conference , 1988, pp. 147–151

1988

Earlier work this paper cites.

S. P. Johnson, D. Amso, and J. A. Slemmer, “Development of object concepts in infancy: Evidence for early learning in an eye-tracking paradigm,” Proceedings of the National Academy of Sciences , vol. 100, no. 18, pp. 10 568–10 573, 2003

2003

Earlier work this paper cites.

L. Smith and M. Gasser, “The development of embodied cognition: Six lessons from babies,” Artificial life , vol. 11, no. 1-2, pp. 13–29, 2005

2005

Earlier work this paper cites.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105

2012

Earlier work this paper cites.

L. Pinto and A. Gupta, “Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours,” in 2016 IEEE international conference on robotics and automation (ICRA) . IEEE, 2016, pp. 3406–3413

2016

Earlier work this paper cites.

L. Pinto, D. Gandhi, Y. Han, Y.-L. Park, and A. Gupta, “The curious robot: Learning visual representations via physical interactions,” in European Conference on Computer Vision . Springer, 2016, pp. 3–18

2016

Earlier work this paper cites.

P. Agrawal, A. V. Nair, P. Abbeel, J. Malik, and S. Levine, “Learning to poke by poking: Experiential learning of intuitive physics,” in Advances in Neural Information Processing Systems , 2016, pp. 5074–5082

2016

Earlier work this paper cites.

A. A. Rusu, N. C. Rabinowitz, G. Desjardins, H. Soyer, J. Kirkpatrick, K. Kavukcuoglu, R. Pascanu, and R. Hadsell, “Progressive neural networks,” arXiv preprint arXiv:1606.04671 , 2016

Original

2016

Earlier work this paper cites.

A. A. Rusu, M. Vecerik, T. Rothörl, N. Heess, R. Pascanu, and R. Hadsell, “Sim-to-real robot learning from pixels with progressive nets,” arXiv preprint arXiv:1610.04286 , 2016

Original

2016

Earlier work this paper cites.

T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2980–2988

2017

Earlier work this paper cites.

K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2961–2969

2017

Earlier work this paper cites.

Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh, “Realtime multi-person 2d pose estimation using part affinity fields,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 7291–7299

2017

Earlier work this paper cites.

R. Calandra, A. Owens, M. Upadhyaya, W. Yuan, J. Lin, E. H. Adelson, and S. Levine, “The feeling of success: Does touch sensing help predict grasp outcomes?” arXiv preprint arXiv:1710.05512 , 2017

Original

2017

Cited alongside, same era.

C. Finn and S. Levine, “Deep visual foresight for planning robot motion,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2017, pp. 2786–2793

2017

Cited alongside, same era.

F. Ebert, C. Finn, A. X. Lee, and S. Levine, “Self-supervised visual planning with temporal skip connections,” arXiv preprint arXiv:1710.05268 , 2017

Original

2017

Cited alongside, same era.

G. Kahn, A. Villaflor, V. Pong, P. Abbeel, and S. Levine, “Uncertainty-aware reinforcement learning for collision avoidance,” arXiv preprint arXiv:1702.01182 , 2017

Original

2017

Cited alongside, same era.

D. Ghosh, A. Gupta, and S. Levine, “Learning actionable representations with goal-conditioned policies,” arXiv preprint arXiv:1811.07819 , 2018

Original

2018

Later among the works it cites.

A. Sax, B. Emi, A. R. Zamir, L. Guibas, S. Savarese, and J. Malik, “Mid-level visual representations improve generalization and sample efficiency for learning active tasks,” arXiv preprint arXiv:1812.11971 , 2018

Original

2018

Later among the works it cites.

R. Girshick, I. Radosavovic, G. Gkioxari, P. Dollár, and K. He, “Detectron,” https://github.com/facebookresearch/detectron , 2018

2018

Later among the works it cites.

D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke, and S. Levine, “Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation,” CoRR , vol. abs/1806.10293, 2018. [Online]. Available: http://arxiv.org/abs/1806.10293

Original

2018

Later among the works it cites.