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Pre-trained generalist policies are rapidly gaining relevance in robot learning due to their promise of fast adaptation to novel, in-domain tasks.
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Sener, O. and Savarese, S · 2017
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Ramasesh, V. V., Lewkowycz, A., and Dyer, E · 2022
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A generalist agent
Reed, S., Zolna, K., Parisotto, E., Colmenarejo, S. G., Novikov, A., Barth-maron, G., Giménez, M., Sulsky, Y., Kay, J., Springenberg, J. T., Eccles, T., Bruce, J., Razavi, A., Edwards, A., Heess, N., Chen, Y., Hadsell, R., Vinyals, O., Bordbar, M., and de Freitas, N · 2022
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Paco: Parameter-compositional multi-task reinforcement learning
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D4rl: Datasets for deep data-driven reinforcement learning
Fu, J., Kumar, A., Nachum, O., Tucker, G., and Levine, S · 2020
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Ho, J., Jain, A., and Abbeel, P · 2020
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Kothawade, S., Beck, N., Killamsetty, K., and Iyer, R · 2020
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Kumar, A., Zhou, A., Tucker, G., and Levine, S · 2020
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Levine, S., Kumar, A., Tucker, G., and Fu, J · 2020
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A general framework for uncertainty estimation in deep learning
Loquercio, A., Segu, M., and Scaramuzza, D · 2020
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Sun, L., Zhang, H., Xu, W., and Tomizuka, M · 2022
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Prediction-oriented bayesian active learning
Bickford Smith, F., Kirsch, A., Farquhar, S., Gal, Y., Foster, A., and Rainforth, T · 2023
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Diffusion policy: Visuomotor policy learning via action diffusion
Chi, C., Feng, S., Du, Y., Xu, Z., Cousineau, E., Burchfiel, B., and Song, S · 2023
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Open x-embodiment: Robotic learning datasets and RT-x models
Collaboration, O. X.-E · 2023
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Multi-task reinforcement learning with mixture of orthogonal experts
Hendawy, A., Peters, J., and D’Eramo, C · 2023
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A framework and benchmark for deep batch active learning for regression
Holzmüller, D., Zaverkin, V., Kästner, J., and Steinwart, I · 2023
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Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., and Cobbe, K · 2023
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Understanding plasticity in neural networks
Lyle, C., Zheng, Z., Nikishin, E., Pires, B. A., Pascanu, R., and Dabney, W · 2023
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Tight performance guarantees of imitator policies with continuous actions
Maran, D., Metelli, A. M., and Restelli, M · 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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Data quality in imitation learning
Belkhale, S., Cui, Y., and Sadigh, D · 2024
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Robocat: A self-improving generalist agent for robotic manipulation
Bousmalis, K., Vezzani, G., Rao, D., Devin, C. M., Lee, A. X., Villalonga, M. B., Davchev, T., Zhou, Y., Gupta, A., Raju, A., Laurens, A., Fantacci, C., Dalibard, V., Zambelli, M., Martins, M. F., Pevceviciute, R., Blokzijl, M., Denil, M., Batchelor, N., Lampe, T., Parisotto, E., Zolna, K., Reed, S., Colmenarejo, S. G., Scholz, J., Abdolmaleki, A., Groth, O., Regli, J.-B., Sushkov, O., Rothörl, T., Chen, J. E., Aytar, Y., Barker, D., Ortiz, J., Riedmiller, M., Springenberg, J. T., Hadsell, R., Nori, F., and Heess, N · 2024
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Scaling rectified flow transformers for high-resolution image synthesis
Esser, P., Kulal, S., Blattmann, A., Entezari, R., Müller, J., Saini, H., Levi, Y., Lorenz, D., Sauer, A., Boesel, F., Podell, D., Dockhorn, T., English, Z., and Rombach, R · 2024
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Is behavior cloning all you need? understanding horizon in imitation learning
Foster, D. J., Block, A., and Misra, D · 2024
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Transductive active learning: Theory and applications
Hübotter, J., Sukhija, B., Treven, L., As, Y., and Krause, A · 2024
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Robohive: A unified framework for robot learning
Kumar, V., Shah, R., Zhou, G., Moens, V., Caggiano, V., Gupta, A., and Rajeswaran, A · 2024
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Evaluating real-world robot manipulation policies in simulation
Li, X., Hsu, K., Gu, J., Pertsch, K., Mees, O., Walke, H. R., Fu, C., Lunawat, I., Sieh, I., Kirmani, S., Levine, S., Wu, J., Finn, C., Su, H., Vuong, Q., and Xiao, T · 2024
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A survey on vision-language-action models for embodied ai
Ma, Y., Song, Z., Zhuang, Y., Hao, J., and King, I · 2024
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Bigger, regularized, optimistic: scaling for compute and sample-efficient continuous control
Nauman, M., Ostaszewski, M., Jankowski, K., Miłoś, P., and Cygan, M · 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., Chen, L. Y., Sanketi, P., Vuong, Q., Xiao, T., Sadigh, D., Finn, C., and Levine, S · 2024
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A comprehensive survey of continual learning: theory, method and application
Wang, L., Zhang, X., Su, H., and Zhu, J · 2024
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Less: Selecting influential data for targeted instruction tuning
Xia, M., Malladi, S., Gururangan, S., Arora, S., and Chen, D · 2024
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Efficiently learning at test-time: Active fine-tuning of llms
Hübotter, J., Bongni, S., Hakimi, I., and Krause, A · 2025
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all-minilm-l6-v2
HuggingFace · 2026
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