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The ability to autonomously learn behaviors via direct interactions in uninstrumented environments can lead to generalist robots capable of enhancing productivity or providing care in unstructured settings like homes.
kpam: Keypoint affordances for category-level robotic manipulation
Manuelli, L., Gao, W., Florence, P., and Tedrake, R · 1903
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
Earlier work this paper cites.
Guided policy search
Levine, S. and Koltun, V · 2013
Earlier work this paper cites.
Playing atari with deep reinforcement learning, 2013
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
Earlier work this paper cites.
Representation learning: A review and new perspectives, 2014
Bengio, Y., Courville, A., and Vincent, P · 2014
Earlier work this paper cites.
Auto-encoding variational bayes, 2014
Kingma, D. P. and Welling, M · 2014
Earlier work this paper cites.
Deep residual learning for image recognition, 2015
He, K., Zhang, X., Ren, S., and Sun, J · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition, 2015
Simonyan, K. and Zisserman, A · 2015
Earlier work this paper cites.
Striving for simplicity: The all convolutional net, 2015
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2015
Earlier work this paper cites.
Rethinking the inception architecture for computer vision, 2015
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2015
Earlier work this paper cites.
Rl 2 : Fast reinforcement learning via slow reinforcement learning, 2016
Duan, Y., Schulman, J., Chen, X., Bartlett, P. L., Sutskever, I., and Abbeel, P · 2016
Earlier work this paper cites.
beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2016
Earlier work this paper cites.
End-to-end training of deep visuomotor policies, 2016
Levine, S., Finn, C., Darrell, T., and Abbeel, P · 2016
Earlier work this paper cites.
Learning dexterous manipulation for a soft robotic hand from human demonstration, 2017
Gupta, A., Eppner, C., Levine, S., and Abbeel, P · 2017
Earlier work this paper cites.
Deep q-learning from demonstrations, 2017
Hester, T., Vecerik, M., Pietquin, O., Lanctot, M., Schaul, T., Piot, B., Horgan, D., Quan, J., Sendonaris, A., Dulac-Arnold, G., Osband, I., Agapiou, J., Leibo, J. Z., and Gruslys, A · 2017
Earlier work this paper cites.
Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Rajeswaran, A., Kumar, V., Gupta, A., Schulman, J., Todorov, E., and Levine, S · 2017
Earlier work this paper cites.
Trust region policy optimization, 2017
Schulman, J., Levine, S., Moritz, P., Jordan, M. I., and Abbeel, P · 2017
Earlier work this paper cites.
Mastering the game of go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T., and Hassabis, D · 2017
Earlier work this paper cites.
Understanding disentangling in β \beta -vae, 2018
Burgess, C. P., Higgins, I., Pal, A., Matthey, L., Watters, N., Desjardins, G., and Lerchner, A · 2018
Earlier work this paper cites.
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Espeholt, L., Soyer, H., Munos, R., Simonyan, K., Mnih, V., Ward, T., Doron, Y., Firoiu, V., Harley, T., Dunning, I., Legg, S., and Kavukcuoglu, K · 2018
Cited alongside, same era.
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Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S · 2018
Cited alongside, same era.
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Higgins, I., Pal, A., Rusu, A. A., Matthey, L., Burgess, C. P., Pritzel, A., Botvinick, M., Blundell, C., and Lerchner, A · 2018
Cited alongside, same era.
Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation, 2018
Kalashnikov, D., Irpan, A., Pastor, P., Ibarz, J., Herzog, A., Jang, E., Quillen, D., Holly, E., Kalakrishnan, M., Vanhoucke, V., and Levine, S · 2018
Cited alongside, same era.
Shufflenet v2: Practical guidelines for efficient cnn architecture design, 2018
Ma, N., Zhang, X., Zheng, H.-T., and Sun, J · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks, 2019
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2019
Later among the works it cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2019
Later among the works it cites.
Robel: Robotics benchmarks for learning with low-cost robots
Ahn, M., Zhu, H., Hartikainen, K., Ponte, H., Gupta, A., Levine, S., and Kumar, V · 2020
Later among the works it cites.
Dream to control: Learning behaviors by latent imagination, 2020
Hafner, D., Lillicrap, T., Ba, J., and Norouzi, M · 2020
Later among the works it cites.
Model-based reinforcement learning for atari, 2020
Kaiser, L., Babaeizadeh, M., Milos, P., Osinski, B., Campbell, R. H., Czechowski, K., Erhan, D., Finn, C., Kozakowski, P., Levine, S., Mohiuddin, A., Sepassi, R., Tucker, G., and Michalewski, H · 2020
Later among the works it cites.
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Visual reinforcement learning with imagined goals, 2018
Nair, A., Pong, V., Dalal, M., Bahl, S., Lin, S., and Levine, S · 2018
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Towards generalization and simplicity in continuous control, 2018
Rajeswaran, A., Lowrey, K., Todorov, E., and Kakade, S · 2018
Cited alongside, same era.
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Sermanet, P., Lynch, C., Chebotar, Y., Hsu, J., Jang, E., Schaal, S., and Levine, S · 2018
Cited alongside, same era.
Deepmind control suite, 2018
Tassa, Y., Doron, Y., Muldal, A., Erez, T., Li, Y., de Las Casas, D., Budden, D., Abdolmaleki, A., Merel, J., Lefrancq, A., Lillicrap, T., and Riedmiller, M · 2018
Cited alongside, same era.
Policy gradient algorithms
Weng, L · 2018
Cited alongside, same era.
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Cited alongside, same era.
Challenges of real-world reinforcement learning
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Later among the works it cites.
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Laskin, M., Lee, K., Stooke, A., Pinto, L., Abbeel, P., and Srinivas, A · 2020
Later among the works it cites.
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Later among the works it cites.
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Srinivas, A., Laskin, M., and Abbeel, P · 2020
Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Soft actor-critic (sac) implementation in pytorch
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Improving sample efficiency in model-free reinforcement learning from images, 2020
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Keypointnet: A large-scale 3d keypoint dataset aggregated from numerous human annotations, 2020
You, Y., Lou, Y., Li, C., Cheng, Z., Li, L., Ma, L., Wang, W., and Lu, C · 2020
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A framework for efficient robotic manipulation, 2020
Zhan, A., Zhao, P., Pinto, L., Abbeel, P., and Laskin, M · 2020
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The ingredients of real-world robotic reinforcement learning, 2020
Zhu, H., Yu, J., Gupta, A., Shah, D., Hartikainen, K., Singh, A., Kumar, V., and Levine, S · 2020
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Very deep vaes generalize autoregressive models and can outperform them on images, 2021
Child, R · 2021
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The distracting control suite – a challenging benchmark for reinforcement learning from pixels, 2021
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