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Cutting-edge robot learning techniques including foundation models and imitation learning from humans all pose huge demands on large-scale and high-quality datasets which constitute one of the bottleneck in the general intelligent robot fields.
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R. Martín-Martín, C. Eppner, and O. Brock, “The rbo dataset of articulated objects and interactions,” The International Journal of Robotics Research , vol. 38, no. 9, pp. 1013–1019, 2019
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X. Xu, M. You, H. Zhou, Z. Qian, and B. He, “Robot imitation learning from image-only observation without real-world interaction,” IEEE/ASME Transactions on Mechatronics , vol. 28, no. 3, pp. 1234–1244, 2022
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J. DelPreto, C. Liu, Y. Luo, M. Foshey, Y. Li, A. Torralba, W. Matusik, and D. Rus, “Actionsense: A multimodal dataset and recording framework for human activities using wearable sensors in a kitchen environment,” Advances in Neural Information Processing Systems , vol. 35, pp. 13 800–13 813, 2022
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R. Dai, S. Das, S. Sharma, L. Minciullo, L. Garattoni, F. Bremond, and G. Francesca, “Toyota smarthome untrimmed: Real-world untrimmed videos for activity detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 2, pp. 2533–2550, 2022
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B. Reily, P. Gao, F. Han, H. Wang, and H. Zhang, “Real-time recognition of team behaviors by multisensory graph-embedded robot learning,” The International Journal of Robotics Research , vol. 41, no. 8, pp. 798–811, 2022
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B. A. Newman, R. M. Aronson, S. S. Srinivasa, K. Kitani, and H. Admoni, “Harmonic: A multimodal dataset of assistive human–robot collaboration,” The International Journal of Robotics Research , vol. 41, no. 1, pp. 3–11, 2022
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A. Zador, S. Escola, B. Richards, B. Ölveczky, Y. Bengio, K. Boahen, M. Botvinick, D. Chklovskii, A. Churchland, C. Clopath et al. , “Catalyzing next-generation artificial intelligence through neuroai,” Nature communications , vol. 14, no. 1, p. 1597, 2023
P. Kang, K. Zhu, S. Jiang, B. He, and P. B. Shull, “Hbod: A novel dataset with synchronized hand, body, and object manipulation data for human-robot interaction,” pp. 1–4, 2023
2023
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2024
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2024
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2024
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2023
Cited alongside, same era.
R. Firoozi, J. Tucker, S. Tian, A. Majumdar, J. Sun, W. Liu, Y. Zhu, S. Song, A. Kapoor, K. Hausman et al. , “Foundation models in robotics: Applications, challenges, and the future,” The International Journal of Robotics Research , p. 02783649241281508, 2023
2023
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2023
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2023
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H.-S. Fang, H. Fang, Z. Tang, J. Liu, J. Wang, H. Zhu, and C. Lu, “Rh20t: A robotic dataset for learning diverse skills in one-shot,” in RSS 2023 Workshop on Learning for Task and Motion Planning , 2023
2023
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H.-S. Fang, M. Gou, C. Wang, and C. Lu, “Robust grasping across diverse sensor qualities: The graspnet-1billion dataset,” The International Journal of Robotics Research , vol. 42, no. 12, pp. 1094–1103, 2023
2023
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2023
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2024
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2024
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2024
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L. Lastrico, V. Belcamino, A. Carfì, A. Vignolo, A. Sciutti, F. Mastrogiovanni, and F. Rea, “The effects of selected object features on a pick-and-place task: A human multimodal dataset,” The International Journal of Robotics Research , vol. 43, no. 1, pp. 98–109, 2024
2024
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