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Pre-trained large language models (LLMs) capture procedural knowledge about the world.
Walk the talk: Connecting language, knowledge, and action in route instructions
MacMahon, M., Stankiewicz, B., and Kuipers, B · 2006
Earlier work this paper cites.
Object goal navigation using goal-oriented semantic exploration, 2020
Chaplot, D. S., Gandhi, D., Gupta, A., and Salakhutdinov, R · 2007
Earlier work this paper cites.
Toward understanding natural language directions
Kollar, T., Tellex, S., Roy, D., and Roy, N · 2010
Earlier work this paper cites.
Alfworld: Aligning text and embodied environments for interactive learning
Shridhar, M., Yuan, X., Côté, M.-A., Bisk, Y., Trischler, A., and Hausknecht, M · 2010
Earlier work this paper cites.
Keep calm and explore: Language models for action generation in text-based games, 2020
Yao, S., Rao, R., Hausknecht, M., and Narasimhan, K · 2010
Earlier work this paper cites.
Understanding natural language commands for robotic navigation and mobile manipulation
Tellex, S., Kollar, T., Dickerson, S., Walter, M., Banerjee, A., Teller, S., and Roy, N · 2011
Earlier work this paper cites.
Deep reinforcement learning with a natural language action space
He, J., Chen, J., He, X., Gao, J., Li, L., Deng, L., and Ostendorf, M · 2015
Earlier work this paper cites.
Listen, attend, and walk: Neural mapping of navigational instructions to action sequences
Mei, H., Bansal, M., and Walter, M. R · 2016
Earlier work this paper cites.
Modular multitask reinforcement learning with policy sketches
Andreas, J., Klein, D., and Levine, S · 2017
Earlier work this paper cites.
What can you do with a rock? affordance extraction via word embeddings
Fulda, N., Ricks, D., Murdoch, B., and Wingate, D · 2017
Earlier work this paper cites.
Mapping instructions and visual observations to actions with reinforcement learning
Misra, D., Langford, J., and Artzi, Y · 2017
Earlier work this paper cites.
Zero-shot task generalization with multi-task deep reinforcement learning
Oh, J., Singh, S., Lee, H., and Kohli, P · 2017
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Get to the point: Summarization with pointer-generator networks
See, A., Liu, P. J., and Manning, C. D · 2017
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Learn what not to learn: Action elimination with deep reinforcement learning
Zahavy, T., Haroush, M., Merlis, N., Mankowitz, D. J., and Mannor, S · 2018
Cited alongside, same era.
Language as an abstraction for hierarchical deep reinforcement learning
Jiang, Y., Gu, S. S., Murphy, K. P., and Finn, C · 2019
Cited alongside, same era.
Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
Pixl2r: Guiding reinforcement learning using natural language by mapping pixels to rewards
Goyal, P., Niekum, S., and Mooney, R · 2021
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Language models are few-shot butlers
Micheli, V. and Fleuret, F · 2021
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Film: Following instructions in language with modular methods, 2021
Min, S. Y., Chaplot, D. S., Ravikumar, P., Bisk, Y., and Salakhutdinov, R · 2021
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Skill induction and planning with latent language
Sharma, P., Torralba, A., and Andreas, J · 2021
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General-purpose question-answering with macaw
Tafjord, O. and Clark, P · 2021
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Cited alongside, same era.
Grounding language to autonomously-acquired skills via goal generation
Akakzia, A., Colas, C., Oudeyer, P.-Y., Chetouani, M., and Sigaud, O · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Higher: Improving instruction following with hindsight generation for experience replay
Cideron, G., Seurin, M., Strub, F., and Pietquin, O · 2020
Cited alongside, same era.
Language-conditioned imitation learning for robot manipulation tasks
Stepputtis, S., Campbell, J., Phielipp, M., Lee, S., Baral, C., and Ben Amor, H · 2020
Cited alongside, same era.
GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow, March 2021
Black, S., Gao, L., Wang, P., Leahy, C., and Biderman, S · 2021
Cited alongside, same era.
Blukis, V., Paxton, C., Fox, D., Garg, A., and Artzi, Y · 2021
Cited alongside, same era.
On the opportunities and risks of foundation models, 2021
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D. E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P. W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X. L., Li, X., Ma, T., Malik, A., Manning, C. D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J. C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J. S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A. W., Tramèr, F., Wang, R. E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S. M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., and Liang, P · 2021
Cited alongside, same era.
Do as i can, not as i say: Grounding language in robotic affordances, 2022
Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., Finn, C., Fu, C., Gopalakrishnan, K., Hausman, K., Herzog, A., Ho, D., Hsu, J., Ibarz, J., Ichter, B., Irpan, A., Jang, E., Ruano, R. J., Jeffrey, K., Jesmonth, S., Joshi, N. J., Julian, R., Kalashnikov, D., Kuang, Y., Lee, K.-H., Levine, S., Lu, Y., Luu, L., Parada, C., Pastor, P., Quiambao, J., Rao, K., Rettinghouse, J., Reyes, D., Sermanet, P., Sievers, N., Tan, C., Toshev, A., Vanhoucke, V., Xia, F., Xiao, T., Xu, P., Xu, S., Yan, M., and Zeng, A · 2022
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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 · 2022
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On grounded planning for embodied tasks with language models
Lin, B. Y., Huang, C., Liu, Q., Gu, W., Sommerer, S., and Ren, X · 2022
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Learning language-conditioned robot behavior from offline data and crowd-sourced annotation
Nair, S., Mitchell, E., Chen, K., Savarese, S., Finn, C., et al · 2022
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Cliport: What and where pathways for robotic manipulation
Shridhar, M., Manuelli, L., and Fox, D · 2022
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Smith, S., Patwary, M., Norick, B., LeGresley, P., Rajbhandari, S., Casper, J., Liu, Z., Prabhumoye, S., Zerveas, G., Korthikanti, V., Zheng, E., Child, R., Aminabadi, R. Y., Bernauer, J., Song, X., Shoeybi, M., He, Y., Houston, M., Tiwary, S., and Catanzaro, B · 2022
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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 · 2022
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