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Embodied agents capable of complex physical skills can improve productivity, elevate life quality, and reshape human-machine collaboration.
Alvinn: An autonomous land vehicle in a neural network
Pomerleau, D. A · 1988
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Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Gu, S., Holly, E., Lillicrap, T., and Levine, S · 2017
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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Rajeswaran, A., Kumar, V., Gupta, A., Vezzani, G., Schulman, J., Todorov, E., and Levine, S · 2017
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Improving language understanding by generative pre-training
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Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G · 2018
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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YOLOv5 by Ultralytics, May 2020
Jocher, G · 2020
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A framework for efficient robotic manipulation
Zhan, A., Zhao, P., Pinto, L., Abbeel, P., and Laskin, M · 2020
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Life span motor development
Haywood, K. M. and Getchell, N · 2021
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BC-Z: zero-shot task generalization with robotic imitation learning
Jang, E., Irpan, A., Khansari, M., Kappler, D., Ebert, F., Lynch, C., Levine, S., and Finn, C · 2021
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Language conditioned imitation learning over unstructured data
Lynch, C. and Sermanet, P · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Yarats, D., Kostrikov, I., and Fergus, R · 2021
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Do as i can, not as i say: Grounding language in robotic affordances
Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., Finn, C., Fu, C., Gopalakrishnan, K., Hausman, K., et al · 2022
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Fang, K., Migimatsu, T., Mandlekar, A., Fei-Fei, L., and Bohg, J · 2022
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Temporal difference learning for model predictive control
Hansen, N., Wang, X., and Su, H · 2022
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Vima: General robot manipulation with multimodal prompts
Jiang, Y., Gupta, A., Zhang, Z., Wang, G., Dou, Y., Chen, Y., Fei-Fei, L., Anandkumar, A., Zhu, Y., and Fan, L · 2022
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Simple but effective: Clip embeddings for embodied ai
Khandelwal, A., Weihs, L., Mottaghi, R., and Kembhavi, A · 2022
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What matters in language conditioned robotic imitation learning over unstructured data
Mees, O., Hermann, L., and Burgard, W · 2022
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Text2motion: From natural language instructions to feasible plans
Lin, K., Agia, C., Migimatsu, T., Pavone, M., and Bohg, J · 2023
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Reflect: Summarizing robot experiences for failure explanation and correction
Liu, Z., Bahety, A., and Song, S · 2023
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Luo, Q., Li, Y., and Wu, Y · 2023
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Eureka: Human-level reward design via coding large language models
Ma, Y. J., Liang, W., Wang, G., Huang, D.-A., Bastani, O., Jayaraman, D., Zhu, Y., Fan, L., and Anandkumar, A · 2023
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Socratic models: Composing zero-shot multimodal reasoning with language
Zeng, A., Attarian, M., Ichter, B., Choromanski, K., Wong, A., Welker, S., Tombari, F., Purohit, A., Ryoo, M., Sindhwani, V., et al · 2022
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Rt-2: Vision-language-action models transfer web knowledge to robotic control
Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Chen, X., Choromanski, K., Ding, T., Driess, D., Dubey, A., Finn, C., et al · 2023
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Towards a unified agent with foundation models
Di Palo, N., Byravan, A., Hasenclever, L., Wulfmeier, M., Heess, N., and Riedmiller, M · 2023
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Palm-e: An embodied multimodal language model
Driess, D., Xia, F., Sajjadi, M. S., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., et al · 2023
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Rvt: Robotic view transformer for 3d object manipulation
Goyal, A., Xu, J., Guo, Y., Blukis, V., Chao, Y.-W., and Fox, D · 2023
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Scaling up and distilling down: Language-guided robot skill acquisition
Ha, H., Florence, P., and Song, S · 2023
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Motor learning and development
Haibach-Beach, P. S., Perreault, M. E., Brian, A. S., and Collier, D. H · 2023
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OpenAI · 2023
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Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action
Shah, D., Osiński, B., Levine, S., et al · 2023
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Progprompt: Generating situated robot task plans using large language models
Singh, I., Blukis, V., Mousavian, A., Goyal, A., Xu, D., Tremblay, J., Fox, D., Thomason, J., and Garg, A · 2023
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Llm-planner: Few-shot grounded planning for embodied agents with large language models
Song, C. H., Wu, J., Washington, C., Sadler, B. M., Chao, W.-L., and Su, Y · 2023
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Large language models as generalizable policies for embodied tasks
Szot, A., Schwarzer, M., Agrawal, H., Mazoure, B., Talbott, W., Metcalf, K., Mackraz, N., Hjelm, D., and Toshev, A · 2023
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Saytap: Language to quadrupedal locomotion
Tang, Y., Yu, W., Tan, J., Zen, H., Faust, A., and Harada, T · 2023
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Gensim: Generating robotic simulation tasks via large language models
Wang, L., Ling, Y., Yuan, Z., Shridhar, M., Bao, C., Qin, Y., Wang, B., Xu, H., and Wang, X · 2023
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Tidybot: Personalized robot assistance with large language models
Wu, J., Antonova, R., Kan, A., Lepert, M., Zeng, A., Song, S., Bohg, J., Rusinkiewicz, S., and Funkhouser, T · 2023
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Text2reward: Automated dense reward function generation for reinforcement learning
Xie, T., Zhao, S., Wu, C. H., Liu, Y., Luo, Q., Zhong, V., Yang, Y., and Yu, T · 2023
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Language to rewards for robotic skill synthesis
Yu, W., Gileadi, N., Fu, C., Kirmani, S., Lee, K.-H., Gonzalez Arenas, M., Lewis Chiang, H.-T., Erez, T., Hasenclever, L., Humplik, J., Ichter, B., Xiao, T., Xu, P., Zeng, A., Zhang, T., Heess, N., Sadigh, D., Tan, J., Tassa, Y., and Xia, F · 2023
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