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The important manifestation of robot intelligence is the ability to naturally interact and autonomously make decisions.
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Karnan, H., Nair, A., Xiao, X., Warnell, G., Pirk, S., Toshev, A., Hart, J., Biswas, J., Stone, P.: Socially compliant navigation dataset (scand): A large-scale dataset of demonstrations for social navigation. IEEE Robotics and Automation Letters 7
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Nair, S., Rajeswaran, A., Kumar, V., Finn, C., Gupta, A.: R3m: A universal visual representation for robot manipulation. In: Conference on Robot Learning (2022), https://api.semanticscholar.org/CorpusID:247618840
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Nair, S., Rajeswaran, A., Kumar, V., Finn, C., Gupta, A.: R3m: A universal visual representation for robot manipulation (2022)
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Gu, J., Kirmani, S., Wohlhart, P., Lu, Y., Arenas, M.G., Rao, K., Yu, W., Fu, C., Gopalakrishnan, K., Xu, Z., Sundaresan, P., Xu, P., Su, H., Hausman, K., Finn, C., Vuong, Q., Xiao, T.: Rt-trajectory: Robotic task generalization via hindsight trajectory sketches (2023)
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2022
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2022
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Shridhar, M., Manuelli, L., Fox, D.: Cliport: What and where pathways for robotic manipulation. In: Conference on Robot Learning. pp. 894–906. PMLR (2022)
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Bahl, S., Mendonca, R., Chen, L., Jain, U., Pathak, D.: Affordances from human videos as a versatile representation for robotics (2023)
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Belkhale, S., Cui, Y., Sadigh, D.: Hydra: Hybrid robot actions for imitation learning. In: Proceedings of the 7th Conference on Robot Learning (CoRL) (2023)
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Bharadhwaj, H., Vakil, J., Sharma, M., Gupta, A., Tulsiani, S., Kumar, V.: Roboagent: Generalization and efficiency in robot manipulation via semantic augmentations and action chunking (2023)
2023
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Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Chen, X., Choromanski, K., Ding, T., Driess, D., Dubey, A., Finn, C., Florence, P., Fu, C., Arenas, M.G., Gopalakrishnan, K., Han, K., Hausman, K., Herzog, A., Hsu, J., Ichter, B., Irpan, A., Joshi, N., Julian, R., Kalashnikov, D., Kuang, Y., Leal, I., Lee, L., Lee, T.W.E., Levine, S., Lu, Y., Michalewski, H., Mordatch, I., Pertsch, K., Rao, K., Reymann, K., Ryoo, M., Salazar, G., Sanketi, P., Sermanet, P., Singh, J., Singh, A., Soricut, R., Tran, H., Vanhoucke, V., Vuong, Q., Wahid, A., Welker, S., Wohlhart, P., Wu, J., Xia, F., Xiao, T., Xu, P., Xu, S., Yu, T., Zitkovich, B.: Rt-2: Vision-language-action models transfer web knowledge to robotic control (2023)
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2023
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2023
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Majumdar, A., Yadav, K., Arnaud, S., Ma, Y.J., Chen, C., Silwal, S., Jain, A., Berges, V.P., Abbeel, P., Malik, J., Batra, D., Lin, Y., Maksymets, O., Rajeswaran, A., Meier, F.: Where are we in the search for an artificial visual cortex for embodied intelligence? (2023)
2023
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Margolis, G.B., Agrawal, P.: Walk these ways: Tuning robot control for generalization with multiplicity of behavior. In: Liu, K., Kulic, D., Ichnowski, J. (eds.) Proceedings of The 6th Conference on Robot Learning. Proceedings of Machine Learning Research, vol. 205, pp. 22–31. PMLR (14–18 Dec 2023)
2023
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Schiavi, G., Wulkop, P., Rizzi, G., Ott, L., Siegwart, R., Chung, J.J.: Learning agent-aware affordances for closed-loop interaction with articulated objects. In: 2023 IEEE International Conference on Robotics and Automation (ICRA). pp. 5916–5922 (2023). https://doi.org/10.1109/ICRA48891.2023.10160747
2023
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Shah, D., Sridhar, A., Dashora, N., Stachowicz, K., Black, K., Hirose, N., Levine, S.: Vint: A foundation model for visual navigation (2023)
2023
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Stone, A., Xiao, T., Lu, Y., Gopalakrishnan, K., Lee, K.H., Vuong, Q., Wohlhart, P., Kirmani, S., Zitkovich, B., Xia, F., Finn, C., Hausman, K.: Open-world object manipulation using pre-trained vision-language models (2023)
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2023
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2023
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2023
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Ahn, M., Dwibedi, D., Finn, C., Arenas, M.G., Gopalakrishnan, K., Hausman, K., Ichter, B., Irpan, A., Joshi, N., Julian, R., Kirmani, S., Leal, I., Lee, E., Levine, S., Lu, Y., Leal, I., Maddineni, S., Rao, K., Sadigh, D., Sanketi, P., Sermanet, P., Vuong, Q., Welker, S., Xia, F., Xiao, T., Xu, P., Xu, S., Xu, Z.: Autort: Embodied foundation models for large scale orchestration of robotic agents (2024)
2024
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