References
- D. Quillen, E. Jang, O. Nachum, C. Finn, J. Ibarz, and S. Levine, "Deep reinforcement learning for vision-based robotic grasping: A simulated comparative evaluation of off-policy methods," 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia, 2018, DOI: 10.1109/icra.2018.8461039.
- 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 visionbased robotic manipulation," arXiv:1806.10293, 2018, [Online], https://arxiv.org/abs/1806.10293.
- T. Kim, Y. Park, Y. Park, and I. H. Suh, "Acceleration of Actor- Critic Deep Reinforcement Learning for Visual Grasping in Clutter by State Representation Learning Based on Disentanglement of a Raw Input Image," arXiv:2002.11903, 2020, [Online], https://arxiv.org/abs/2002.11903.
- J. Mahler, M. Matl, X. Liu, A. Li, D. Gealy, and K. Goldberg, "Dex-Net 3.0: Computing robust vacuum suction grasp targets in point clouds using a new analytic model and deep learning," 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia, 2018, DOI: 10.1109/icra.2018.8460887.
- S. Levine, P. Pastor, A. Krizhevsky, and D. Quillen, "Learning hand-eye coordination for robotic grasping with large-scale data collection," International Symposium on Experimental Robotics, pp. 173-184, 2016, DOI: 10.1007/978-3-319-50115-4_16.
- C. Finn, X. Y. Tan, Y. Duan, T. Darrell, S. Levine, and P. Abbeel, "Deep spatial autoencoders for visuomotor learning," 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 2016, DOI: 10.1109/icra.2016.7487173.
- H. van Hoof, N. Chen, M. Karl, P. van der Smagt, and J. Peters, "Stable reinforcement learning with autoencoders for tactile and visual data," 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, South Korea, 2016, DOI: 10.1109/iros.2016.7759578.
- I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner, "beta-VAE: Learning basic visual concepts with a constrained variational framework," ICLR, 2017, [Online], https://www.semanticscholar.org/paper/beta-VAE%3A-Learning-Basic-Visual-Concepts-with-a-Higgins-Matthey/a90226c41b79f8b06007609f39f82757073641e2.
- K. Bousmalis, A. Irpan, P. Wohlhart, Y. Bai, M. Kelcey, M. Kalakrishnan, L. Downs, J. Ibarz, P. Pastor, K. Konolige, S. Levine, and V. Vanhoucke, "Using simulation and domain adaptation to improve efficiency of deep robotic grasping," 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia, 2018, DOI: 10.1109/ICRA.2018.8460875.
- G. Barth-Maron, M. W. Hoffman, D. Budden, W. Dabney, D. Horgan, A. Muldal, N. Heess, and T. Lillicrap, "Distributed distributional deterministic policy gradients," arXiv:1804.08617, 2018, [Online], https://arxiv.org/abs/1804.08617.
- T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, "Continuous control with deep reinforcement learning," arXiv:1509.02971, 2016, [Online], https://arxiv.org/abs/1509.02971.
- P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol, "Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion," Journal of Machine Learning Research, 2010, [Online], https://www.semanticscholar.org/paper/Stacked-Denoising-Autoencoders%3A-Learning-Useful-in-Vincent-Larochelle/e2b7f37cd97a7907b1b8a41138721ed06a0b76cd.
- J. Redmon and A. Farhadi, "Yolov3: An incremental improvement," arXiv:1804.02767, 2018, [Online], https://arxiv.org/abs/1804.02767.
- D. P. Kingma and M. Welling, "Auto-encoding variational Bayes," arXiv:1312.6114, 2013, [Online], https://arxiv.org/abs/1312.6114.
- S. Ren, K. He, R. Grisshick, and J. Sun, "Faster R-CNN: Toward Real-Time Object Detection with Region Proposal Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, DOI: 10.1109/tpami.2016.2577031.
- A. Lukezic, T. Vojir, L. C. Zaic, J. Matas, and M. Krstan, "Discriminative Correlation Filter Tracker with Channel and Spatial Reliability," International Journal of Computer Vision, 2019, DOI: 10.1007/s11263-017-1061-3.
- V. Badrinarayanan, A. Kendall, and R. Cipolla, "Segnet: a deep convolutional encoder-decoder architecture for image segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 12, pp. 2481-2495, 2017, DOI: 10.1109/tpami.2016.2644615.