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Recently developed pretrained models can encode rich world knowledge expressed in multiple modalities, such as text and images.
Language Models are Few-shot Learners
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NuSMV 2: An OpenSource Tool for Symbolic Model Checking. In Computer Aided Verification (Lecture Notes in Computer Science, Vol. 2404) . Springer, 359–364
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You Only Look Once: Unified, Real-Time Object Detection. In Conference on Computer Vision and Pattern Recognition . IEEE Computer Society, Las Vegas, NV, USA, 779–788
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. 2017 · 2017
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Argoverse: 3D Tracking and Forecasting With Rich Maps. In IEEE Conference on Computer Vision and Pattern Recognition . Computer Vision Foundation / IEEE, Long Beach, CA, USA, 8748–8757
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, Minneapolis, MN, USA, 4171–4186
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An Introduction to the Planning Domain Definition Language
Patrik Haslum, Nir Lipovetzky, Daniele Magazzeni, and Christian Muise. 2019 · 2019
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A Framework for Formal Verification of Behavior Trees with Linear Temporal Logic
Oliver Biggar and Mohammad Zamani. 2020 · 2020
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Evaluating Large Language Models Trained on Code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan, Harrison Edwards, Yura Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, and et al. 2021 · 2021
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Learning Transferable Visual Models From Natural Language Supervision. In International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 139) , Marina Meila and Tong Zhang (Eds.). PMLR, Virtual, 8748–8763
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021 · 2021
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PaLM: Scaling Language Modeling with Pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, and et al. 2022 · 2022
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Open-vocabulary Object Detection via Vision and Language Knowledge Distillation. In International Conference on Learning Representations . OpenReview.net, Virtual, 1–20
Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. 2022 · 2022
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Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization
Mutian He, Tianqing Fang, Weiqi Wang, and Yangqiu Song. 2022 · 2022
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Do As I Can, Not As I Say: Grounding Language in Robotic Affordances. In Conference on Robot Learning (Proceedings of Machine Learning Research, Vol. 205) . PLMR, Auckland, New Zealand, 287–318
Brian Ichter, Anthony Brohan, Yevgen Chebotar, Chelsea Finn, Karol Hausman, Alexander Herzog, Daniel Ho, Julian Ibarz, Alex Irpan, Eric Jang, Ryan Julian, Dmitry Kalashnikov, Sergey Levine, Yao Lu, Carolina Parada, Kanishka Rao, Pierre Sermanet, Alexander Toshev, Vincent Vanhoucke, Fei Xia, Ted Xiao, Peng Xu, Mengyuan Yan, Noah Brown, Michael Ahn, Omar Cortes, Nicolas Sievers, Clayton Tan, Sichun Xu, Diego Reyes, Jarek Rettinghouse, Jornell Quiambao, Peter Pastor, Linda Luu, Kuang-Huei Lee, Yuheng Kuang, Sally Jesmonth, Nikhil J. Joshi, Kyle Jeffrey, Rosario Jauregui Ruano, Jasmine Hsu, Keerthana Gopalakrishnan, Byron David, Andy Zeng, and Chuyuan Kelly Fu. 2022 · 2022
ProgPrompt: Generating Situated Robot Task Plans using Large Language Models
Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg. 2022 · 2022
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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model
Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Anand Korthikanti, Elton Zhang, Rewon Child, Reza Yazdani Aminabadi, Julie Bernauer, Xia Song, Mohammad Shoeybi, Yuxiong He, Michael Houston, Saurabh Tiwary, and Bryan Catanzaro. 2022 · 2022
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LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models
Chan Hee Song, Jiaman Wu, Clay Washington, Brian M. Sadler, Wei-Lun Chao, and Yu Su. 2022 · 2022
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Symbolic Knowledge Distillation: from General Language Models to Commonsense Models. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . Association for Computational Linguistics, Seattle, WA, United States, 4602–4625
Peter West, Chandra Bhagavatula, Jack Hessel, Jena D. Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, and Yejin Choi. 2022 · 2022
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Cited alongside, same era.
Grounded Language-Image Pre-training. In Conference on Computer Vision and Pattern Recognition . IEEE, New Orleans, LA, USA, 10955–10965
Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, Kai-Wei Chang, and Jianfeng Gao. 2022 · 2022
Cited alongside, same era.
Neuro-Symbolic Procedural Planning with Commonsense Prompting
Yujie Lu, Weixi Feng, Wanrong Zhu, Wenda Xu, Xin Eric Wang, Miguel Eckstein, and William Yang Wang. 2022 · 2022
Cited alongside, same era.
Utilizing Language Models to Expand Vision-Based Commonsense Knowledge Graphs
Navid Rezaei and Marek Z. Reformat. 2022 · 2022
Cited alongside, same era.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Teven Le Scao, Angela Fan, Christopher Akiki, Elizabeth-Jane Pavlick, Suzana Ili’c, Daniel Hesslow, Roman Castagn’e, Alexandra Sasha Luccioni, Franccois Yvon, Matthias Gallé, and et al. 2022 · 2022
Cited alongside, same era.
LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action. In Conference on Robot Learning (Proceedings of Machine Learning Research, Vol. 205) . PMLR, Auckland, New Zealand, 492–504
Dhruv Shah, Blazej Osinski, Brian Ichter, and Sergey Levine. 2022 · 2022
Cited alongside, same era.
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents. In International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 162) . PMLR, Baltimore, Maryland, USA, 9118–9147
Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch. 2022a
Cited in the paper.
Inner Monologue: Embodied Reasoning through Planning with Language Models. In Conference on Robot Learning (Proceedings of Machine Learning Research, Vol. 205) . PLMR, Auckland, New Zealand, 1769–1782
Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, Pierre Sermanet, Tomas Jackson, Noah Brown, Linda Luu, Sergey Levine, Karol Hausman, and Brian Ichter. 2022b
Cited in the paper.
On Grounded Planning for Embodied Tasks with Language Models. In AAAI Conference on Artificial Intelligence , Brian Williams, Yiling Chen, and Jennifer Neville (Eds.). AAAI Press, Washington, DC, USA, 13192–13200
Bill Yuchen Lin, Chengsong Huang, Qian Liu, Wenda Gu, Sam Sommerer, and Xiang Ren. 2023b
Cited in the paper.
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Automaton-Based Representations of Task Knowledge from Generative Language Models
Yunhao Yang, Jean-Raphael Gaglione, Cyrus Neary, and Ufuk Topcu. 2022 · 2022
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Segment Anything
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick. 2023 · 2023
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ChatGPT for Robotics: Design Principles and Model Abilities
Sai Vemprala, Rogerio Bonatti, Arthur Bucker, and Ashish Kapoor. 2023 · 2023
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Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents
Zihao Wang, Shaofei Cai, Anji Liu, Xiaojian Ma, and Yitao Liang. 2023 · 2023
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