Fetching the paper…
Reading the bibliography…
Learning sensorimotor control policies from high-dimensional images crucially relies on the quality of the underlying visual representations.
Reinforcement learning with augmented data
Laskin, M., Lee, K., Stooke, A., Pinto, L., Abbeel, P., and Srinivas, A · 2004
Earlier work this paper cites.
Curl: Contrastive unsupervised representations for reinforcement learning
Laskin, M., Srinivas, A., and Abbeel, P · 2004
Earlier work this paper cites.
Core knowledge
Spelke, E. S. and Kinzler, K. D · 2007
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
Earlier work this paper cites.
Shapenet: An information-rich 3d model repository, 2015
Chang, A. X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., and Yu, F · 2015
Earlier work this paper cites.
Reinforcement learning with unsupervised auxiliary tasks
Jaderberg, M., Mnih, V., Czarnecki, W. M., Schaul, T., Leibo, J. Z., Silver, D., and Kavukcuoglu, K · 2016
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D · 2016
Earlier work this paper cites.
Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., and Kavukcuoglu, K · 2016
Earlier work this paper cites.
Wavenet: A generative model for raw audio
Oord, A. v. d., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K · 2016
Earlier work this paper cites.
Learning human behaviors for robot-assisted dressing
Clegg, A., Yu, W., Tan, J., Kemp, C. C., Turk, G., and Liu, C. K · 2017
Earlier work this paper cites.
Curiosity-driven exploration by self-supervised prediction
Pathak, D., Agrawal, P., Efros, A. A., and Darrell, T · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Cited alongside, same era.
Dense object nets: Learning dense visual object descriptors by and for robotic manipulation
Florence, P. R., Manuelli, L., and Tedrake, R · 2018
Cited alongside, same era.
Unsupervised learning of object landmarks through conditional image generation
Unsupervised learning of object structure and dynamics from videos
Minderer, M., Sun, C., Villegas, R., Cole, F., Murphy, K., and Lee, H · 2019
Later among the works it cites.
Animating arbitrary objects via deep motion transfer, 2019
Siarohin, A., Lathuilière, S., Tulyakov, S., Ricci, E., and Sebe, N · 2019
Later among the works it cites.
Self-supervised 3d keypoint learning for ego-motion estimation
Tang, J., Ambrus, R., Guizilini, V., Pillai, S., Kim, H., Jensfelt, P., and Gaidon, A · 2019
Later among the works it cites.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning, 2019
Yu, T., Quillen, D., He, Z., Julian, R., Hausman, K., Finn, C., and Levine, S · 2019
Later among the works it cites.
Assistive gym: A physics simulation framework for assistive robotics
Erickson, Z., Gangaram, V., Kapusta, A., Liu, C. K., and Kemp, C. C · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jakab, T., Gupta, A., Bilen, H., and Vedaldi, A · 2018
Cited alongside, same era.
Discovery of latent 3d keypoints via end-to-end geometric reasoning
Suwajanakorn, S., Snavely, N., Tompson, J., and Norouzi, M · 2018
Cited alongside, same era.
Large-scale study of curiosity-driven learning
Burda, Y., Edwards, H., Pathak, D., Storkey, A., Darrell, T., and Efros, A. A · 2019
Cited alongside, same era.
Pybullet, a python module for physics simulation for games, robotics and machine learning
Coumans, E. and Bai, Y · 2019
Cited alongside, same era.
kpam-sc: Generalizable manipulation planning using keypoint affordance and shape completion
Gao, W. and Tedrake, R · 2019
Cited alongside, same era.
Unsupervised keypoint learning for guiding class-conditional video prediction
Kim, Y., Nam, S., Cho, I., and Kim, S. J · 2019
Cited alongside, same era.
Unsupervised learning of object keypoints for perception and control
Kulkarni, T. D., Gupta, A., Ionescu, C., Borgeaud, S., Reynolds, M., Zisserman, A., and Mnih, V · 2019
Cited alongside, same era.
kpam: Keypoint affordances for category-level robotic manipulation
Manuelli, L., Gao, W., Florence, P., and Tedrake, R · 2019
Cited alongside, same era.
Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Kostrikov, I., Yarats, D., and Fergus, R · 2020
Later among the works it cites.
Keypoints into the future: Self-supervised correspondence in model-based reinforcement learning
Manuelli, L., Li, Y., Florence, P., and Tedrake, R · 2020
Later among the works it cites.
First order motion model for image animation
Siarohin, A., Lathuilière, S., Tulyakov, S., Ricci, E., and Sebe, N · 2020
Later among the works it cites.
Learning rope manipulation policies using dense object descriptors trained on synthetic depth data
Sundaresan, P., Grannen, J., Thananjeyan, B., Balakrishna, A., Laskey, M., Stone, K., Gonzalez, J. E., and Goldberg, K · 2020
Later among the works it cites.
Deepsfm: Structure from motion via deep bundle adjustment
Wei, X., Zhang, Y., Li, Z., Fu, Y., and Xue, X · 2020
Later among the works it cites.
Learning deep network for detecting 3d object keypoints and 6d poses
Zhao, W., Zhang, S., Guan, Z., Zhao, W., Peng, J., and Fan, J · 2020
Later among the works it cites.