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Existing robot policies predominantly adopt the task-centric approach, requiring end-to-end task data collection.
A. Marzinotto, M. Colledanchise, C. Smith, and P. Ögren, “Towards a unified behavior trees framework for robot control,” in
2014
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S. Hangl, S. Stabinger, and J. Piater, “Autonomous skill-centric testing using deep learning,” in
2017
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A. Radford and K. Narasimhan, “Improving language understanding by generative pre-training,”
2018
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A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever,
2019
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F. Xia, C. Li, R. Martín-Martín, O. Litany, A. Toshev, and S. Savarese, “Relmogen: Integrating motion generation in reinforcement learning for mobile manipulation,” in
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,
2021
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C. Sun, J. Orbik, C. M. Devin, B. H. Yang, A. Gupta, G. Berseth, and S. Levine, “Fully autonomous real-world reinforcement learning with applications to mobile manipulation,” in
2022
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P. Ögren and C. I. Sprague, “Behavior trees in robot control systems,”
2022
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M. Ahn, A. Brohan, N. Brown, Y. Chebotar,
2022
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2022
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W. Huang, P. Abbeel, D. Pathak, and I. Mordatch, “Language models as zero-shot planners: Extracting actionable knowledge for embodied agents,” in
2022
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D. Shah, P. Xu, Y. Lu, T. Xiao, A. T. Toshev, S. Levine,
2022
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S. Macenski, T. Foote, B. Gerkey, C. Lalancette, and W. Woodall, “Robot operating system 2: Design, architecture, and uses in the wild,”
2022
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H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” 2023
2023
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H. Liu, C. Li, Y. Li, and Y. J. Lee, “Improved baselines with visual instruction tuning,” 2023
2023
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2023
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B. Zitkovich, T. Yu, S. Xu, P. Xu, T. Xiao, F. Xia, J. Wu, P. Wohlhart, S. Welker, A. Wahid, Q. Vuong, V. Vanhoucke, H. T. Tran, R. Soricut, A. Singh, J. Singh, P. Sermanet, P. R. Sanketi, G. Salazar, M. S. Ryoo, K. Reymann, K. Rao, K. Pertsch, I. Mordatch, H. Michalewski, Y. Lu, S. Levine, L. Lee, T. E. Lee, I. Leal, Y. Kuang, D. Kalashnikov, R. Julian, N. J. Joshi, A. Irpan, B. Ichter, J. Hsu, A. Herzog, K. Hausman, K. Gopalakrishnan, C. Fu, P. Florence, C. Finn, K. A. Dubey, D. Driess, T. Ding, K. M. Choromanski, X. Chen, Y. Chebotar, J. Carbajal, N. Brown, A. Brohan, M. G. Arenas, and K. Han, “RT-2: vision-language-action models transfer web knowledge to robotic control,” in
2023
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2023
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D. Honerkamp, T. Welschehold, and A. Valada, “N$
2023
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T. Z. Zhao, V. Kumar, S. Levine, and C. Finn, “Learning fine-grained bimanual manipulation with low-cost hardware,” in
2023
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C. Chi, Z. Xu, S. Feng, E. Cousineau, Y. Du, B. Burchfiel, R. Tedrake, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,”
2023
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C. H. Song, J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y. Su, “Llm-planner: Few-shot grounded planning for embodied agents with large language models,” in
2023
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I. Singh, V. Blukis, A. Mousavian, A. Goyal, D. Xu, J. Tremblay, D. Fox, J. Thomason, and A. Garg, “Progprompt: Generating situated robot task plans using large language models,” in
2023
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Y. Hu, F. Lin, T. Zhang, L. Yi, and Y. Gao, “Look before you leap: Unveiling the power of gpt-4v in robotic vision-language planning,” in
2023
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2023
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J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng, “Code as policies: Language model programs for embodied control,” in
2023
Cited alongside, same era.
W. Huang, C. Wang, R. Zhang, Y. Li, J. Wu, and L. Fei-Fei, “Voxposer: Composable 3d value maps for robotic manipulation with language models,” in
2024
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2024
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J. Luo, C. Xu, X. Geng, G. Feng, K. Fang, L. Tan, S. Schaal, and S. Levine, “Multi-stage cable routing through hierarchical imitation learning,”
2024
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A. Mei, G.-N. Zhu, H. Zhang, and Z. Gan, “Replanvlm: Replanning robotic tasks with visual language models,”
2024
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2023
Cited alongside, same era.
A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, J. Dabis, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, J. Hsu, J. Ibarz, B. Ichter, A. Irpan, T. Jackson, S. Jesmonth, N. J. Joshi, R. Julian, D. Kalashnikov, Y. Kuang, I. Leal, K. Lee, S. Levine, Y. Lu, U. Malla, D. Manjunath, I. Mordatch, O. Nachum, C. Parada, J. Peralta, E. Perez, K. Pertsch, J. Quiambao, K. Rao, M. S. Ryoo, G. Salazar, P. R. Sanketi, K. Sayed, J. Singh, S. Sontakke, A. Stone, C. Tan, H. T. Tran, V. Vanhoucke, S. Vega, Q. Vuong, F. Xia, T. Xiao, P. Xu, S. Xu, T. Yu, and B. Zitkovich, “RT-1: robotics transformer for real-world control at scale,” in
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
D. Driess, F. Xia, M. S. M. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Tompson, Q. Vuong, T. Yu, W. Huang, Y. Chebotar, P. Sermanet, D. Duckworth, S. Levine, V. Vanhoucke, K. Hausman, M. Toussaint, K. Greff, A. Zeng, I. Mordatch, and P. Florence, “Palm-e: An embodied multimodal language model,” in
2023
Cited alongside, same era.
M. Shridhar, L. Manuelli, and D. Fox, “Perceiver-actor: A multi-task transformer for robotic manipulation,” in
2023
Cited alongside, same era.
B. Peng, C. Li, P. He, M. Galley, and J. Gao, “Instruction tuning with gpt-4,”
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2024
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Z. Fu, T. Z. Zhao, and C. Finn, “Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation,” in
2024
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C. Chi, Z. Xu, C. Pan, E. Cousineau, B. Burchfiel, S. Feng, R. Tedrake, and S. Song, “Universal manipulation interface: In-the-wild robot teaching without in-the-wild robots,” in
2024
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2024
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Z. Wang, S. Cai, G. Chen, A. Liu, X. S. Ma, and Y. Liang, “Describe, explain, plan and select: interactive planning with llms enables open-world multi-task agents,”
2024
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2024
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2024
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2024
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Y. Mu, Q. Zhang, M. Hu, W. Wang, M. Ding, J. Jin, B. Wang, J. Dai, Y. Qiao, and P. Luo, “Embodiedgpt: Vision-language pre-training via embodied chain of thought,”
2024
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S. H. Vemprala, R. Bonatti, A. Bucker, and A. Kapoor, “Chatgpt for robotics: Design principles and model abilities,”
2024
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B. Chen, Z. Xu, S. Kirmani, B. Ichter, D. Sadigh, L. Guibas, and F. Xia, “Spatialvlm: Endowing vision-language models with spatial reasoning capabilities,” in
2024
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2024
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J. Wen, Y. Zhu, J. Li, M. Zhu, K. Wu, Z. Xu, R. Cheng, C. Shen, Y. Peng, F. Feng,
2024
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X. Li, M. Liu, H. Zhang, C. Yu, J. Xu, H. Wu, C. Cheang, Y. Jing, W. Zhang, H. Liu, H. Li, and T. Kong, “Vision-language foundation models as effective robot imitators,” in
2024
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Z. Xu, Y. Zhang, E. Xie, Z. Zhao, Y. Guo, K.-Y. K. Wong, Z. Li, and H. Zhao, “Drivegpt4: Interpretable end-to-end autonomous driving via large language model,”
2024
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T. Cheng, L. Song, Y. Ge, W. Liu, X. Wang, and Y. Shan, “Yolo-world: Real-time open-vocabulary object detection,” in
2024
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T. Ren, Q. Jiang, S. Liu, Z. Zeng, W. Liu, H. Gao, H. Huang, Z. Ma, X. Jiang, Y. Chen, Y. Xiong, H. Zhang, F. Li, P. Tang, K. Yu, and L. Zhang, “Grounding dino 1.5: Advance the ”edge” of open-set object detection,” 2024
2024
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