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Humans can quickly learn new behaviors by leveraging background world knowledge.
Does string-based neural MT learn source syntax?
X. Shi, I. Padhi, and K. Knight · 2016
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Distributional reinforcement learning with quantile regression, 2017
W. Dabney, M. Rowland, M. G. Bellemare, and R. Munos · 2017
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Proximal policy optimization algorithms, 2017
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Attention is all you need, 2017
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2017
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Grounding language for transfer in deep reinforcement learning, 2018
K. Narasimhan, R. Barzilay, and T. Jaakkola · 2018
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Analysis methods in neural language processing: A survey, 2019
Y. Belinkov and J. Glass · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding, 2019
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
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What does BERT learn about the structure of language?
G. Jawahar, B. Sagot, and D. Seddah · 2019
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Habitat: A Platform for Embodied AI Research
M. Savva, A. Kadian, O. Maksymets, Y. Zhao, E. Wijmans, B. Jain, J. Straub, J. Liu, V. Koltun, J. Malik, D. Parikh, and D. Batra · 2019
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Bert rediscovers the classical nlp pipeline, 2019
I. Tenney, D. Das, and E. Pavlick · 2019
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Does bert make any sense? interpretable word sense disambiguation with contextualized embeddings, 2019
G. Wiedemann, S. Remus, A. Chawla, and C. Biemann · 2019
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Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei · 2020
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Conservative q-learning for offline reinforcement learning, 2020
A. Kumar, A. Zhou, G. Tucker, and S. Levine · 2020
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Transporter networks: Rearranging the visual world for robotic manipulation
A. Zeng, P. Florence, J. Tompson, S. Welker, J. Chien, M. Attarian, T. Armstrong, I. Krasin, D. Duong, V. Sindhwani, and J. Lee · 2020
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Emerging properties in self-supervised vision transformers, 2021
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin · 2021
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Implicit representations of meaning in neural language models, 2021
B. Z. Li, M. Nye, and J. Andreas · 2021
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Language conditioned imitation learning over unstructured data, 2021
C. Lynch and P. Sermanet · 2021
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Learning transferable visual models from natural language supervision, 2021
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever · 2021
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Stable-baselines3: Reliable reinforcement learning implementations
A. Raffin, A. Hill, A. Gleave, A. Kanervisto, M. Ernestus, and N. Dormann · 2021
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Habitat-matterport 3d dataset (hm3d): 1000 large-scale 3d environments for embodied ai, 2021
S. K. Ramakrishnan, A. Gokaslan, E. Wijmans, O. Maksymets, A. Clegg, J. Turner, E. Undersander, W. Galuba, A. Westbury, A. X. Chang, M. Savva, Y. Zhao, and D. Batra · 2021
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Cliport: What and where pathways for robotic manipulation
M. Shridhar, L. Manuelli, and D. Fox · 2021
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Do as i can and not as i say: Grounding language in robotic affordances
M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, C. Fu, K. Gopalakrishnan, K. Hausman, A. Herzog, D. Ho, J. Hsu, J. Ibarz, B. Ichter, A. Irpan, E. Jang, R. J. Ruano, K. Jeffrey, S. Jesmonth, N. Joshi, R. Julian, D. Kalashnikov, Y. Kuang, K.-H. Lee, S. Levine, Y. Lu, L. Luu, C. Parada, P. Pastor, J. Quiambao, K. Rao, J. Rettinghouse, D. Reyes, P. Sermanet, N. Sievers, C. Tan, A. Toshev, V. Vanhoucke, F. Xia, T. Xiao, P. Xu, S. Xu, M. Yan, and A. Zeng · 2022
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Video pretraining (vpt): Learning to act by watching unlabeled online videos, 2022
B. Baker, I. Akkaya, P. Zhokhov, J. Huizinga, J. Tang, A. Ecoffet, B. Houghton, R. Sampedro, and J. Clune · 2022
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Affordance learning from play for sample-efficient policy learning
J. Borja-Diaz, O. Mees, G. Kalweit, L. Hermann, J. Boedecker, and W. Burgard · 2022
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Latte: Language trajectory transformer, 2022
A. Bucker, L. Figueredo, S. Haddadin, A. Kapoor, S. Ma, S. Vemprala, and R. Bonatti · 2022
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Minedojo: Building open-ended embodied agents with internet-scale knowledge
L. Fan, G. Wang, Y. Jiang, A. Mandlekar, Y. Yang, H. Zhu, A. Tang, D.-A. Huang, Y. Zhu, and A. Anandkumar · 2022
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Language models (mostly) know what they know, 2022
S. Kadavath, T. Conerly, A. Askell, T. Henighan, D. Drain, E. Perez, N. Schiefer, Z. Hatfield-Dodds, N. DasSarma, E. Tran-Johnson, S. Johnston, S. El-Showk, A. Jones, N. Elhage, T. Hume, A. Chen, Y. Bai, S. Bowman, S. Fort, D. Ganguli, D. Hernandez, J. Jacobson, J. Kernion, S. Kravec, L. Lovitt, K. Ndousse, C. Olsson, S. Ringer, D. Amodei, T. Brown, J. Clark, N. Joseph, B. Mann, S. McCandlish, C. Olah, and J. Kaplan · 2022
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Minerl diamond 2021 competition: Overview, results, and lessons learned, 2022
A. Kanervisto, S. Milani, K. Ramanauskas, N. Topin, Z. Lin, J. Li, J. Shi, D. Ye, Q. Fu, W. Yang, W. Hong, Z. Huang, H. Chen, G. Zeng, Y. Lin, V. Micheli, E. Alonso, F. Fleuret, A. Nikulin, Y. Belousov, O. Svidchenko, and A. Shpilman · 2022
Cited alongside, same era.
Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation, 2022
Visual language maps for robot navigation
C. Huang, O. Mees, A. Zeng, and W. Burgard · 2023
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Survey of hallucination in natural language generation
Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. J. Bang, A. Madotto, and P. Fung · 2023
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Segment anything, 2023
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollár, and R. Girshick · 2023
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Code as policies: Language model programs for embodied control, 2023
J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng · 2023
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Steve-1: A generative model for text-to-behavior in minecraft, 2023
S. Lifshitz, K. Paster, H. Chan, J. Ba, and S. McIlraith · 2023
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Instruction-following agents with multimodal transformer, 2023
H. Liu, L. Lee, K. Lee, and P. Abbeel · 2023
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J. Li, D. Li, C. Xiong, and S. Hoi · 2022
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Stubborn: A strong baseline for indoor object navigation, 2022
H. Luo, A. Yue, Z.-W. Hong, and P. Agrawal · 2022
Cited alongside, same era.
R3m: A universal visual representation for robot manipulation, 2022
S. Nair, A. Rajeswaran, V. Kumar, C. Finn, and A. Gupta · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback, 2022
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, and R. Lowe · 2022
Cited alongside, same era.
Habitat-web: Learning embodied object-search strategies from human demonstrations at scale, 2022
R. Ramrakhya, E. Undersander, D. Batra, and A. Das · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models, 2022
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer · 2022
Cited alongside, same era.
Skill induction and planning with latent language, 2022
P. Sharma, A. Torralba, and J. Andreas · 2022
Cited alongside, same era.
Progprompt: Generating situated robot task plans using large language models, 2022
I. Singh, V. Blukis, A. Mousavian, A. Goyal, D. Xu, J. Tremblay, D. Fox, J. Thomason, and A. Garg · 2022
Cited alongside, same era.
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Where are we in the search for an artificial visual cortex for embodied intelligence?, 2023
A. Majumdar, K. Yadav, S. Arnaud, Y. J. Ma, C. Chen, S. Silwal, A. Jain, V.-P. Berges, P. Abbeel, J. Malik, D. Batra, Y. Lin, O. Maksymets, A. Rajeswaran, and F. Meier · 2023
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Grounding language with visual affordances over unstructured data
O. Mees, J. Borja-Diaz, and W. Burgard · 2023
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Goal representations for instruction following: A semi-supervised language interface to control, 2023
V. Myers, A. He, K. Fang, H. Walke, P. Hansen-Estruch, C.-A. Cheng, M. Jalobeanu, A. Kolobov, A. Dragan, and S. Levine · 2023
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Do embodied agents dream of pixelated sheep: Embodied decision making using language guided world modelling, 2023
K. Nottingham, P. Ammanabrolu, A. Suhr, Y. Choi, H. Hajishirzi, S. Singh, and R. Fox · 2023
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Octo: An open-source generalist robot policy
O.M.T., D. Ghosh, H. Walke, K. Pertsch, K. Black, O. Mees, S. Dasari, J. Hejna, C. Xu, J. Luo, T. Kreiman, Y. Tan, D. Sadigh, C. Finn, and S. Levine · 2023
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Towards a unified agent with foundation models, 2023
N. D. Palo, A. Byravan, L. Hasenclever, M. Wulfmeier, N. Heess, and M. Riedmiller · 2023
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Pirlnav: Pretraining with imitation and rl finetuning for objectnav, 2023
R. Ramrakhya, D. Batra, E. Wijmans, and A. Das · 2023
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Correcting robot plans with natural language feedback
P. Sharma, B. Sundaralingam, V. Blukis, C. Paxton, T. Hermans, A. Torralba, J. Andreas, and D. Fox · 2023
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Chatgpt for robotics: Design principles and model abilities
S. Vemprala, R. Bonatti, A. Bucker, and A. Kapoor · 2023
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Chain-of-thought prompting elicits reasoning in large language models, 2023
J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. Le, and D. Zhou · 2023
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Habitat challenge 2023
K. Yadav, J. Krantz, R. Ramrakhya, S. K. Ramakrishnan, J. Yang, A. Wang, J. Turner, A. Gokaslan, V.-P. Berges, R. Mootaghi, O. Maksymets, A. X. Chang, M. Savva, A. Clegg, D. S. Chaplot, and D. Batra · 2023
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Plan4mc: Skill reinforcement learning and planning for open-world minecraft tasks, 2023
H. Yuan, C. Zhang, H. Wang, F. Xie, P. Cai, H. Dong, and Z. Lu · 2023
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Sigmoid loss for language image pre-training, 2023
X. Zhai, B. Mustafa, A. Kolesnikov, and L. Beyer · 2023
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Learning from visual observation via offline pretrained state-to-go transformer, 2023
B. Zhou, K. Li, J. Jiang, and Z. Lu · 2023
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Ghost in the minecraft: Generally capable agents for open-world environments via large language models with text-based knowledge and memory, 2023
X. Zhu, Y. Chen, H. Tian, C. Tao, W. Su, C. Yang, G. Huang, B. Li, L. Lu, X. Wang, Y. Qiao, Z. Zhang, and J. Dai · 2023
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Prismatic vlms: Investigating the design space of visually-conditioned language models, 2024
S. Karamcheti, S. Nair, A. Balakrishna, P. Liang, T. Kollar, and D. Sadigh · 2024
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Large language models as generalizable policies for embodied tasks, 2024
A. Szot, M. Schwarzer, H. Agrawal, B. Mazoure, W. Talbott, K. Metcalf, N. Mackraz, D. Hjelm, and A. Toshev · 2024
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