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Designing modern imitation learning (IL) policies requires making numerous decisions, including the selection of feature encoding, architecture, policy representation, and more.
Alvinn: An autonomous land vehicle in a neural network
Pomerleau, D. A · 1988
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Learning from demonstration
Schaal, S · 1996
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Language conditioned imitation learning over unstructured data, 2021
Lynch, C. and Sermanet, P · 2005
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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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An image is worth 16x16 words: Transformers for image recognition at scale, 2021
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2010
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Language-conditioned imitation learning for robot manipulation tasks, 2020
Stepputtis, S., Campbell, J., Phielipp, M., Lee, S., Baral, C., and Amor, H. B · 2010
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Deep residual learning for image recognition, 2015
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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U-net: Convolutional networks for biomedical image segmentation, 2015
Ronneberger, O., Fischer, P., and Brox, T · 2015
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Generative adversarial imitation learning
Ho, J. and Ermon, S · 2016
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C. R., Su, H., Mo, K., and Guibas, L. J · 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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An algorithmic perspective on imitation learning
Osa, T., Pajarinen, J., Neumann, G., Bagnell, J. A., Abbeel, P., Peters, J., et al · 2018
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Behavioral cloning from observation
Torabi, F., Warnell, G., and Stone, P · 2018
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Hydra - a framework for elegantly configuring complex applications
Yadan, O · 2019
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Experiment tracking with weights and biases, 2020
Biewald, L · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Learning latent plans from play
Lynch, C., Khansari, M., Xiao, T., Kumar, V., Tompson, J., Levine, S., and Sermanet, P · 2020
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Is space-time attention all you need for video understanding?, 2021
Bertasius, G., Wang, H., and Torresani, L · 2021
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Decision transformer: Reinforcement learning via sequence modeling, 2021
Chen, L., Lu, K., Rajeswaran, A., Lee, K., Grover, A., Laskin, M., Abbeel, P., Srinivas, A., and Mordatch, I · 2021
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What matters in learning from offline human demonstrations for robot manipulation
Mandlekar, A., Xu, D., Wong, J., Nasiriany, S., Wang, C., Kulkarni, R., Fei-Fei, L., Savarese, S., Zhu, Y., and Martín-Martín, R · 2021
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Learning transferable visual models from natural language supervision, 2021
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I · 2021
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Visual imitation made easy
Young, S., Gandhi, D., Tulsiani, S., Gupta, A., Abbeel, P., and Pinto, L · 2021
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Building normalizing flows with stochastic interpolants
Albergo, M. S. and Vanden-Eijnden, E · 2022
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Du, S., Luo, Y., Chen, W., Xu, J., and Zeng, D · 2022
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Using both demonstrations and language instructions to efficiently learn robotic tasks, 2023
Yu, A. and Mooney, R. J · 2023
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Learning fine-grained bimanual manipulation with low-cost hardware
Zhao, T. Z., Kumar, V., Levine, S., and Finn, C · 2023
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Viola: Imitation learning for vision-based manipulation with object proposal priors, 2023
Zhu, Y., Joshi, A., Stone, P., and Zhu, Y · 2023
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xlstm: Extended long short-term memory, 2024
Beck, M., Pöppel, K., Spanring, M., Auer, A., Prudnikova, O., Kopp, M., Klambauer, G., Brandstetter, J., and Hochreiter, S · 2024
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Gleave, A., Taufeeque, M., Rocamonde, J., Jenner, E., Wang, S. H., Toyer, S., Ernestus, M., Belrose, N., Emmons, S., and Russell, S · 2022
Cited alongside, same era.
Bc-z: Zero-shot task generalization with robotic imitation learning, 2022
Jang, E., Irpan, A., Khansari, M., Kappler, D., Ebert, F., Lynch, C., Levine, S., and Finn, C · 2022
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Flow matching for generative modeling
Lipman, Y., Chen, R. T., Ben-Hamu, H., Nickel, M., and Le, M · 2022
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Flow straight and fast: Learning to generate and transfer data with rectified flow
Liu, X., Gong, C., and Liu, Q · 2022
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What matters in language conditioned robotic imitation learning over unstructured data, 2022
Mees, O., Hermann, L., and Burgard, W · 2022
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Shaier, S., Raissi, M., and Seshaiyer, P · 2022
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Cliport: What and where pathways for robotic manipulation
Shridhar, M., Manuelli, L., and Fox, D · 2022
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Film-ensemble: Probabilistic deep learning via feature-wise linear modulation, 2022
Turkoglu, M. O., Becker, A., Gündüz, H. A., Rezaei, M., Bischl, B., Daudt, R. C., D’Aronco, S., Wegner, J. D., and Schindler, K · 2022
Cited alongside, same era.
Bharadhwaj, H., Vakil, J., Sharma, M., Gupta, A., Tulsiani, S., and Kumar, V · 2024
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Cleandiffuser: An easy-to-use modularized library for diffusion models in decision making
Dong, Z., Yuan, Y., Hao, J., Ni, F., Ma, Y., Li, P., and Zheng, Y · 2024
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Mamba: Linear-time sequence modeling with selective state spaces, 2024
Gu, A. and Dao, T · 2024
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Pointpatchrl – masked reconstruction improves reinforcement learning on point clouds, 2024
Gyenes, B., Franke, N., Becker, P., and Neumann, G · 2024
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Mail: Improving imitation learning with mamba, 2024
Jia, X., Wang, Q., Donat, A., Xing, B., Li, G., Zhou, H., Celik, O., Blessing, D., Lioutikov, R., and Neumann, G · 2024
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3d diffuser actor: Policy diffusion with 3d scene representations
Ke, T.-W., Gkanatsios, N., and Fragkiadaki, K · 2024
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Openvla: An open-source vision-language-action model, 2024
Kim, M. J., Pertsch, K., Karamcheti, S., Xiao, T., Balakrishna, A., Nair, S., Rafailov, R., Foster, E., Lam, G., Sanketi, P., Vuong, Q., Kollar, T., Burchfiel, B., Tedrake, R., Sadigh, D., Levine, S., Liang, P., and Finn, C · 2024
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Behavior generation with latent actions
Lee, S., Wang, Y., Etukuru, H., Kim, H. J., Shafiullah, N. M. M., and Pinto, L · 2024
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Towards generalist robot policies: What matters in building vision-language-action models
Li, X., Li, P., Liu, M., Wang, D., Liu, J., Kang, B., Ma, X., Kong, T., Zhang, H., and Liu, H · 2024
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Robocasa: Large-scale simulation of everyday tasks for generalist robots, 2024
Nasiriany, S., Maddukuri, A., Zhang, L., Parikh, A., Lo, A., Joshi, A., Mandlekar, A., and Zhu, Y · 2024
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Octo: An open-source generalist robot policy
Octo Model Team, Ghosh, D., Walke, H., Pertsch, K., Black, K., Mees, O., Dasari, S., Hejna, J., Xu, C., Luo, J., Kreiman, T., Tan, Y., Sanketi, P., Vuong, Q., Xiao, T., Sadigh, D., Finn, C., and Levine, S · 2024
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Flow matching imitation learning for multi-support manipulation, 2024
Rouxel, Q., Ferrari, A., Ivaldi, S., and Mouret, J.-B · 2024
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Wan, W., Zhu, Y., Shah, R., and Zhu, Y · 2024
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Any-point trajectory modeling for policy learning, 2024
Wen, C., Lin, X., So, J., Chen, K., Dou, Q., Gao, Y., and Abbeel, P · 2024
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Enerverse: Envisioning embodied future space for robotics manipulation, 2025
Huang, S., Chen, L., Zhou, P., Chen, S., Jiang, Z., Hu, Y., Gao, P., Li, H., Yao, M., and Ren, G · 2025
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