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Clear, interpretable instructions are invaluable when attempting any complex task.
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J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 779–788
2016
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
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E. Perez, F. Strub, H. De Vries, V. Dumoulin, and A. Courville, “Film: Visual reasoning with a general conditioning layer,” in Proceedings of the AAAI conference on artificial intelligence , vol. 32, no. 1, 2018
2018
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2018
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R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 586–595
2018
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M. Danielczuk, A. Kurenkov, A. Balakrishna, M. Matl, D. Wang, R. Martin-Martin, A. Garg, S. Savarese, and K. Goldberg, “Mechanical search: Multi-step retrieval of a target object occluded by clutter,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, May 2019. [Online]. Available: http://dx.doi.org/10.1109/ICRA.2019.8794143
2019
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L. Shao, T. Migimatsu, Q. Zhang, K. Yang, and J. Bohg, “Concept2robot: Learning manipulation concepts from instructions and human demonstrations,” in Proceedings of Robotics: Science and Systems (RSS) , 2020
2020
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M. Seitzer, “pytorch-fid: FID Score for PyTorch,” https://github.com/mseitzer/pytorch-fid , 2020
2020
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F. Ebert, Y. Yang, K. Schmeckpeper, B. Bucher, G. Georgakis, K. Daniilidis, C. Finn, and S. Levine, “Bridge data: Boosting generalization of robotic skills with cross-domain datasets,” 2021
2021
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P. Florence, C. Lynch, A. Zeng, O. Ramirez, A. Wahid, L. Downs, A. Wong, J. Lee, I. Mordatch, and J. Tompson, “Implicit behavioral cloning,” Conference on Robot Learning (CoRL) , 2021
2021
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C. Lynch and P. Sermanet, “Language conditioned imitation learning over unstructured data,” 2021
2021
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A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
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2021
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2021
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2021
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2021
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M. Shridhar, L. Manuelli, and D. Fox, “Perceiver-actor: A multi-task transformer for robotic manipulation,” 2022
2022
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C. Lynch, A. Wahid, J. Tompson, T. Ding, J. Betker, R. Baruch, T. Armstrong, and P. Florence, “Interactive language: Talking to robots in real time,” 2022
2022
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E. Jang, A. Irpan, M. Khansari, D. Kappler, F. Ebert, C. Lynch, S. Levine, and C. Finn, “Bc-z: Zero-shot task generalization with robotic imitation learning,” 2022
2022
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K. Burns, T. Yu, C. Finn, and K. Hausman, “Robust manipulation with spatial features,” in CoRL 2022 Workshop on Pre-training Robot Learning , 2022. [Online]. Available: https://openreview.net/forum?id=X7beXNWxYP
2022
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2022
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2023
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L. Zhang, A. Rao, and M. Agrawala, “Adding conditional control to text-to-image diffusion models,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 3836–3847
2023
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A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo et al. , “Segment anything,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 4015–4026
2023
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Z. Dai, Z. Zhang, Y. Yao, B. Qiu, S. Zhu, L. Qin, and W. Wang, “Animateanything: Fine-grained open domain image animation with motion guidance,” arXiv e-prints , pp. arXiv–2311, 2023
2023
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T. Karras, M. Aittala, T. Aila, and S. Laine, “Elucidating the design space of diffusion-based generative models,” Advances in Neural Information Processing Systems , vol. 35, pp. 26 565–26 577, 2022
2022
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R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
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C. Chen and J. Mo, “IQA-PyTorch: Pytorch toolbox for image quality assessment,” [Online]. Available: https://github.com/chaofengc/IQA-PyTorch , 2022
2022
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I. Skorokhodov, S. Tulyakov, and M. Elhoseiny, “Stylegan-v: A continuous video generator with the price, image quality and perks of stylegan2,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 3626–3636
2022
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2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
H. R. Walke, K. Black, T. Z. Zhao, Q. Vuong, C. Zheng, P. Hansen-Estruch, A. W. He, V. Myers, M. J. Kim, M. Du et al. , “Bridgedata v2: A dataset for robot learning at scale,” in Conference on Robot Learning . PMLR, 2023, pp. 1723–1736
2023
Cited alongside, same era.
J. Yang, M. Gao, Z. Li, S. Gao, F. Wang, and F. Zheng, “Track anything: Segment anything meets videos,” 2023
2023
Later among the works it cites.
P. Sundaresan, Q. Vuong, J. Gu, P. Xu, T. Xiao, S. Kirmani, T. Yu, M. Stark, A. Jain, K. Hausman, D. Sadigh, J. Bohg, and S. Schaal, “Rt-sketch: Goal-conditioned imitation learning from hand-drawn sketches,” 2024
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Y. Du, S. Yang, B. Dai, H. Dai, O. Nachum, J. Tenenbaum, D. Schuurmans, and P. Abbeel, “Learning universal policies via text-guided video generation,” Advances in Neural Information Processing Systems , vol. 36, 2024
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