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This paper shows that self-supervised visual pre-training from real-world images is effective for learning motor control tasks from pixels.
Dimensionality reduction by learning an invariant mapping
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The ycb object and model set: Towards common benchmarks for manipulation research
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Human-level control through deep reinforcement learning
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Unsupervised learning of visual representations using videos
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Learning to poke by poking: Experiential learning of intuitive physics
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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End-to-end training of deep visuomotor policies
Levine, S., Finn, C., Darrell, T., and Abbeel, P · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M. and Favaro, P · 2016
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Context encoders: Feature learning by inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., and Efros, A. A · 2016
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Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
Pinto, L. and Gupta, A · 2016
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Wavenet: A generative model for raw audio
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Colorful image colorization
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Mask r-cnn
He, K., Gkioxari, G., Dollár, P., and Girshick, R · 2017
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Reinforcement learning with unsupervised auxiliary tasks
Jaderberg, M., Mnih, V., Czarnecki, W. M., Schaul, T., Leibo, J. Z., Silver, D., and Kavukcuoglu, K · 2017
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Self-normalizing neural networks
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Playing fps games with deep reinforcement learning
Lample, G. and Chaplot, D. S · 2017
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Learning to navigate in complex environments
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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Loss is its own reward: Self-supervision for reinforcement learning
Shelhamer, E., Mahmoudieh, P., Argus, M., and Darrell, T · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Scaling egocentric vision: The epic-kitchens dataset
Damen, D., Doughty, H., Farinella, G. M., Fidler, S., Furnari, A., Kazakos, E., Moltisanti, D., Munro, J., Perrett, T., Price, W., et al · 2018
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Dense object nets: Learning dense visual object descriptors by and for robotic manipulation
Florence, P. R., Manuelli, L., and Tedrake, R · 2018
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Ha, D. and Schmidhuber, J · 2018
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Scalable deep reinforcement learning for vision-based robotic manipulation
Kalashnikov, D., Irpan, A., Pastor, P., Ibarz, J., Herzog, A., Jang, E., Quillen, D., Holly, E., Kalakrishnan, M., Vanhoucke, V., et al · 2018
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Unsupervised representation learning by predicting image rotations
Komodakis, N. and Gidaris, S · 2018
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Space-time correspondence as a contrastive random walk
Jabri, A., Owens, A., and Efros, A · 2020
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Rlbench: The robot learning benchmark & learning environment
James, S., Ma, Z., Arrojo, D. R., and Davison, A. J · 2020
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Reinforcement learning with augmented data
Laskin, M., Lee, K., Stooke, A., Pinto, L., Abbeel, P., and Srinivas, A · 2020
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Learning dexterous in-hand manipulation
OpenAI, Andrychowicz, M., Baker, B., Chociej, M., Józefowicz, R., McGrew, B., Pachocki, J., Petron, A., Plappert, M., Powell, G., Ray, A., Schneider, J., Sidor, S., Tobin, J., Welinder, P., Weng, L., and Zaremba, W · 2020
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Understanding human hands in contact at internet scale
Shan, D., Geng, J., Shu, M., and Fouhey, D. F · 2020
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Curl: Contrastive unsupervised representations for reinforcement learning
Srinivas, A., Laskin, M., and Abbeel, P · 2020
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Oord, A. v. d., Li, Y., and Vinyals, O · 2018
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I · 2018
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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Rajeswaran, A., Kumar, V., Gupta, A., Vezzani, G., Schulman, J., Todorov, E., and Levine, S · 2018
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Sax, A., Emi, B., Zamir, A. R., Guibas, L., Savarese, S., and Malik, J · 2018
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Time-contrastive networks: Self-supervised learning from video
Sermanet, P., Lynch, C., Chebotar, Y., Hsu, J., Jang, E., Schaal, S., Levine, S., and Brain, G · 2018
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Tassa, Y., Doron, Y., Muldal, A., Erez, T., Li, Y., Casas, D. d. L., Budden, D., Abdolmaleki, A., Merel, J., Lefrancq, A., et al · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Wu, Z., Xiong, Y., Yu, S. X., and Lin, D · 2018
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Learning to see before learning to act: Visual pre-training for manipulation
Yen-Chen, L., Zeng, A., Song, S., Isola, P., and Lin, T.-Y · 2020
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Yu, T., Quillen, D., He, Z., Julian, R., Hausman, K., Finn, C., and Levine, S · 2020
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A framework for efficient robotic manipulation
Zhan, A., Zhao, P., Pinto, L., Abbeel, P., and Laskin, M · 2020
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robosuite: A modular simulation framework and benchmark for robot learning
Zhu, Y., Wong, J., Mandlekar, A., and Martín-Martín, R · 2020
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Deep reinforcement learning at the edge of the statistical precipice
Agarwal, R., Schwarzer, M., Castro, P. S., Courville, A. C., and Bellemare, M · 2021
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Rescaling egocentric vision: Collection, pipeline and challenges for epic-kitchens-100
Damen, D., Doughty, H., Farinella, G. M., , Furnari, A., Ma, J., Kazakos, E., Moltisanti, D., Munro, J., Perrett, T., Price, W., and Wray, M · 2021
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Ego4d: Around the world in 3,000 hours of egocentric video
Grauman, K., Westbury, A., Byrne, E., Chavis, Z., Furnari, A., Girdhar, R., Hamburger, J., Jiang, H., Liu, M., Liu, X., et al · 2021
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Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R · 2021
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Generalization in dexterous manipulation via geometry-aware multi-task learning
Huang, W., Mordatch, I., Abbeel, P., and Pathak, D · 2021
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Isaac gym: High performance gpu-based physics simulation for robot learning
Makoviychuk, V., Wawrzyniak, L., Guo, Y., Lu, M., Storey, K., Macklin, M., Hoeller, D., Rudin, N., Allshire, A., Handa, A., et al · 2021
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Dexvip: Learning dexterous grasping with human hand pose priors from video
Mandikal, P. and Grauman, K · 2021
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The surprising effectiveness of representation learning for visual imitation
Pari, J., Muhammad, N., Arunachalam, S. P., Pinto, L., et al · 2021
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Dexmv: Imitation learning for dexterous manipulation from human videos
Qin, Y., Wu, Y.-H., Liu, S., Jiang, H., Yang, R., Fu, Y., and Wang, X · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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State-only imitation learning for dexterous manipulation
Radosavovic, I., Wang, X., Pinto, L., and Malik, J · 2021
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Learning to walk in minutes using massively parallel deep reinforcement learning
Rudin, N., Hoeller, D., Reist, P., and Hutter, M · 2021
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Rrl: Resnet as representation for reinforcement learning
Shah, R. and Kumar, V · 2021
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Beit: Bert pre-training of image transformers
Bao, H., Dong, L., and Wei, F · 2022
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