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Behavior Cloning (BC) is a widely adopted visual imitation learning method in robot manipulation.
Asymptotic evaluation of certain markov process expectations for large time. iv
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A framework for behavioural cloning
Bain, M. and Sammut, C · 1995
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Cover, T. M · 1999
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The information bottleneck method
Tishby, N., Pereira, F. C., and Bialek, W · 1999
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A survey of robot learning from demonstration
Argall, B. D., Chernova, S., Veloso, M., and Browning, B · 2009
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Image segmentation using information bottleneck method
Bardera, A., Rigau, J., Boada, I., Feixas, M., and Sbert, M · 2009
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Attention is all you need
Vaswani, A · 2017
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Mutual information neural estimation
Belghazi, M. I., Baratin, A., Rajeshwar, S., Ozair, S., Bengio, Y., Courville, A., and Hjelm, D · 2018
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Film: Visual reasoning with a general conditioning layer
Perez, E., Strub, F., De Vries, H., Dumoulin, V., and Courville, A · 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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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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Behavioral cloning from observation
Torabi, F., Warnell, G., and Stone, P · 2018
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Learning representations for neural network-based classification using the information bottleneck principle
Amjad, R. A. and Geiger, B. C · 2019
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Learning robust representations via multi-view information bottleneck
Federici, M., Dutta, A., Forré, P., Kushman, N., and Akata, Z · 2019
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Curiosity-bottleneck: Exploration by distilling task-specific novelty
Kim, Y., Nam, W., Kim, H., Kim, J.-H., and Kim, G · 2019
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REPRESENTATION COMPRESSION AND GENERALIZATION IN DEEP NEURAL NETWORKS, 2019
Shwartz-Ziv, R., Painsky, A., and Tishby, N · 2019
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Learning task-driven control policies via information bottlenecks
Pacelli, V. and Majumdar, A · 2020
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Fighting copycat agents in behavioral cloning from observation histories
Wen, C., Lin, J., Darrell, T., Jayaraman, D., and Gao, 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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Dynamic bottleneck for robust self-supervised exploration
Bai, C., Wang, L., Han, L., Garg, A., Hao, J., Liu, P., and Wang, Z · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A · 2021
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Ib-gan: Disentangled representation learning with information bottleneck generative adversarial networks
Jeon, I., Lee, W., Pyeon, M., and Kim, G · 2021
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Reducing information bottleneck for weakly supervised semantic segmentation
Lee, J., Choi, J., Mok, J., and Yoon, S · 2021
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Multi-view information-bottleneck representation learning
Wan, Z., Zhang, C., Zhu, P., and Hu, Q · 2021
Cited alongside, same era.
Trifinger: An open-source robot for learning dexterity
Wuthrich, M., Widmaier, F., Grimminger, F., Joshi, S., Agrawal, V., Hammoud, B., Khadiv, M., Bogdanovic, M., Berenz, V., Viereck, J., et al · 2021
Bridging the sim-to-real gap from the information bottleneck perspective
He, H., Wu, P., Bai, C., Lai, H., Wang, L., Pan, L., Hu, X., and Zhang, W · 2024
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Video prediction policy: A generalist robot policy with predictive visual representations
Hu, Y., Guo, Y., Wang, P., Chen, X., Wang, Y.-J., Zhang, J., Sreenath, K., Lu, C., and Chen, J · 2024
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Chain-of-thought predictive control
Jia, Z., Thumuluri, V., Liu, F., Chen, L., Huang, Z., and Su, H · 2024
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Openvla: An open-source vision-language-action model
Kim, M. J., Pertsch, K., Karamcheti, S., Xiao, T., Balakrishna, A., Nair, S., Rafailov, R., Foster, E., Lam, G., Sanketi, P., et al · 2024
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Libero: Benchmarking knowledge transfer for lifelong robot learning
Liu, B., Zhu, Y., Gao, C., Feng, Y., Liu, Q., Zhu, Y., and Stone, P · 2024
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Cited alongside, same era.
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 · 2022
Cited alongside, same era.
Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R · 2022
Cited alongside, same era.
Bc-z: Zero-shot task generalization with robotic imitation learning
Jang, E., Irpan, A., Khansari, M., Kappler, D., Ebert, F., Lynch, C., Levine, S., and Finn, C · 2022
Cited alongside, same era.
Counter-strike deathmatch with large-scale behavioural cloning
Pearce, T. and Zhu, J · 2022
Cited alongside, same era.
Rt-2: Vision-language-action models transfer web knowledge to robotic control
Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Chen, X., Choromanski, K., Ding, T., Driess, D., Dubey, A., Finn, C., et al · 2023
Cited alongside, same era.
What makes pre-trained visual representations successful for robust manipulation?
Burns, K., Witzel, Z., Hamid, J. I., Yu, T., Finn, C., and Hausman, K · 2023
Cited alongside, same era.
Contrastive imitation learning for language-guided multi-task robotic manipulation
Ma, T., Zhou, J., Wang, Z., Qiu, R., and Liang, J · 2024
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Multimodal diffusion transformer: Learning versatile behavior from multimodal goals
Reuss, M., Yağmurlu, Ö. E., Wenzel, F., and Lioutikov, R · 2024
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Scaling proprioceptive-visual learning with heterogeneous pre-trained transformers
Wang, L., Chen, X., Zhao, J., and He, K · 2024
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Unleashing large-scale video generative pre-training for visual robot manipulation
Wu, H., Jing, Y., Cheang, C., Chen, G., Xu, J., Li, X., Liu, M., Li, H., and Kong, T · 2024
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Robotic control via embodied chain-of-thought reasoning
Zawalski, M., Chen, W., Pertsch, K., Mees, O., Finn, C., and Levine, S · 2024
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3d diffusion policy: Generalizable visuomotor policy learning via simple 3d representations
Ze, Y., Zhang, G., Zhang, K., Hu, C., Wang, M., and Xu, H · 2024
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Learning manipulation by predicting interaction
Zeng, J., Bu, Q., Wang, B., Xia, W., Chen, L., Dong, H., Song, H., Wang, D., Hu, D., Luo, P., et al · 2024
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Hirt: Enhancing robotic control with hierarchical robot transformers
Zhang, J., Guo, Y., Chen, X., Wang, Y.-J., Hu, Y., Shi, C., and Chen, J · 2024
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Spa: 3d spatial-awareness enables effective embodied representation
Zhu, H., Yang, H., Wang, Y., Yang, J., Wang, L., and He, T · 2024
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Cui, C., Ding, P., Song, W., Bai, S., Tong, X., Ge, Z., Suo, R., Zhou, W., Liu, Y., Jia, B., et al · 2025
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Video prediction policy: A generalist robot policy with predictive visual representations
Hu, Y., Guo, Y., Wang, P., Chen, X., Wang, Y.-J., Zhang, J., Sreenath, K., Lu, C., and Chen, J · 2025
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What matters in learning from large-scale datasets for robot manipulation
Saxena, V., Bronars, M., Arachchige, N. R., Wang, K., Shin, W. C., Nasiriany, S., Mandlekar, A., and Xu, D · 2025
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Accelerating vision-language-action model integrated with action chunking via parallel decoding
Song, W., Chen, J., Ding, P., Zhao, H., Zhao, W., Zhong, Z., Ge, Z., Ma, J., and Li, H · 2025
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Predictive inverse dynamics models are scalable learners for robotic manipulation
Tian, Y., Yang, S., Zeng, J., Wang, P., Lin, D., Dong, H., and Pang, J · 2025
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Dexvla: Vision-language model with plug-in diffusion expert for general robot control
Wen, J., Zhu, Y., Li, J., Tang, Z., Shen, C., and Feng, F · 2025
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Vlas: Vision-language-action model with speech instructions for customized robot manipulation
Zhao, W., Ding, P., Zhang, M., Gong, Z., Bai, S., Zhao, H., and Wang, D · 2025
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