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Significant advances have been made in developing general-purpose embodied AI in environments like Minecraft through the adoption of LLM-augmented hierarchical approaches.
Parameter-efficient transfer learning for nlp, 2019
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 1902
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Frontier-based exploration using multiple robots
Brian Yamauchi · 1998
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Human-level control through deep reinforcement learning
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The malmo platform for artificial intelligence experimentation
Matthew Johnson, Katja Hofmann, Tim Hutton, and David Bignell · 2016
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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#exploration: A study of count-based exploration for deep reinforcement learning, 2017
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, and Pieter Abbeel · 2017
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Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime G. Carbonell, Quoc V. Le, and Ruslan Salakhutdinov · 2019
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Minerl: A large-scale dataset of minecraft demonstrations
William H Guss, Brandon Houghton, Nicholay Topin, Phillip Wang, Cayden Codel, Manuela Veloso, and Ruslan Salakhutdinov · 2019
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Towards mental time travel: a hierarchical memory for reinforcement learning agents
Andrew Kyle Lampinen, Stephanie C Y Chan, Andrea Banino, and Felix Hill · 2021
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Juewu-mc: Playing minecraft with sample-efficient hierarchical reinforcement learning
Zichuan Lin, Junyou Li, Jianing Shi, Deheng Ye, Qiang Fu, and Wei Yang · 2021
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Learning transferable visual models from natural language supervision, 2021
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
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Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al · 2022
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Avalon: A Benchmark for RL Generalization Using Procedurally Generated Worlds
Josh Albrecht, Abraham J. Fetterman, Bryden Fogelman, Ellie Kitanidis, Bartosz Wróblewski, Nicole Seo, Michael Rosenthal, Maksis Knutins, Zachary Polizzi, James B. Simon, and Kanjun Qiu · 2022
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Video pretraining (vpt): Learning to act by watching unlabeled online videos
Bowen Baker, Ilge Akkaya, Peter Zhokov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, and Jeff Clune · 2022
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Revisiting DP-means: Fast scalable algorithms via parallelism and delayed cluster creation
Or Dinari and Oren Freifeld · 2022
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Seihai: A sample-efficient hierarchical ai for the minerl competition
Hangyu Mao, Chao Wang, Xiaotian Hao, Yihuan Mao, Yiming Lu, Chengjie Wu, Jianye Hao, Dong Li, and Pingzhong Tang · 2022
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Xizhou Zhu, Yuntao Chen, Hao Tian, Chenxin Tao, Weijie Su, Chenyu Yang, Gao Huang, Bin Li, Lewei Lu, Xiaogang Wang, Yu Qiao, Zhaoxiang Zhang, and Jifeng Dai · 2023
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Spatially-aware transformers for embodied agents
Junmo Cho, Jaesik Yoon, and Sungjin Ahn · 2024
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Rap: Retrieval-augmented planning with contextual memory for multimodal llm agents, 2024
Tomoyuki Kagaya, Thong Jing Yuan, Yuxuan Lou, Jayashree Karlekar, Sugiri Pranata, Akira Kinose, Koki Oguri, Felix Wick, and Yang You · 2024
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Optimus-1: Hybrid multimodal memory empowered agents excel in long-horizon tasks, 2024
Zaijing Li, Yuquan Xie, Rui Shao, Gongwei Chen, Dongmei Jiang, and Liqiang Nie · 2024
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Steve-1: A generative model for text-to-behavior in minecraft
Shalev Lifshitz, Keiran Paster, Harris Chan, Jimmy Ba, and Sheila McIlraith · 2024
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Matthew Chang, Theophile Gervet, Mukul Khanna, Sriram Yenamandra, Dhruv Shah, So Yeon Min, Kavit Shah, Chris Paxton, Saurabh Gupta, Dhruv Batra, Roozbeh Mottaghi, Jitendra Malik, and Devendra Singh Chaplot · 2023
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Llm-planner: Few-shot grounded planning for embodied agents with large language models
Chan Hee Song, Jiaman Wu, Clayton Washington, Brian M. Sadler, Wei-Lun Chao, and Yu Su · 2023
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Llama: Open and efficient foundation language models, 2023
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample · 2023
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Animal-ai 3: What’s new & why you should care, 2023
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Plan4MC: Skill reinforcement learning and planning for open-world Minecraft tasks
Haoqi Yuan, Chi Zhang, Hongcheng Wang, Feiyang Xie, Penglin Cai, Hao Dong, and Zongqing Lu · 2023
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Odyssey: Empowering agents with open-world skills
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Mp5: A multi-modal open-ended embodied system in minecraft via active perception
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Pre-training goal-based models for sample-efficient reinforcement learning
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