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Recent embodied agents are primarily built based on reinforcement learning (RL) or large language models (LLMs).
Embodied artificial intelligence
Chrisley, R · 2003
Earlier work this paper cites.
Prismarinejs/mineflayer: Create minecraft bots with a powerful, stable, and high level javascript api
PrismarineJS · 2013
Earlier work this paper cites.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
Earlier work this paper cites.
Improving deep reinforcement learning in minecraft with action advice
Frazier, S. and Riedl, M · 2019
Earlier work this paper cites.
Minerl: a large-scale dataset of minecraft demonstrations
Guss, W. H., Houghton, B., Topin, N., Wang, P., Codel, C., Veloso, M., and Salakhutdinov, R · 2019
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Kenton, J. D. M.-W. C. and Toutanova, L. K · 2019
Earlier work this paper cites.
Habitat: A platform for embodied ai research
Savva, M., Kadian, A., Maksymets, O., Zhao, Y., Wijmans, E., Jain, B., Straub, J., Liu, J., Koltun, V., Malik, J., et al · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Sample efficient reinforcement learning through learning from demonstrations in minecraft
Scheller, C., Schraner, Y., and Vogel, M · 2020
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Hu, E. J., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al · 2022
Cited alongside, same era.
Video pretraining (vpt): Learning to act by watching unlabeled online videos
Baker, B., Akkaya, I., Zhokov, P., Huizinga, J., Tang, J., Ecoffet, A., Houghton, B., Sampedro, R., and Clune, J · 2022
Cited alongside, same era.
Visual language maps for robot navigation
Huang, C., Mees, O., Zeng, A., and Burgard, W · 2023
Later among the works it cites.
Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al · 2023
Later among the works it cites.
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
Later among the works it cites.
Skill reinforcement learning and planning for open-world long-horizon tasks
Yuan, H., Zhang, C., Wang, H., Xie, F., Cai, P., Dong, H., and Lu, Z · 2023
Later among the works it cites.
See and think: Embodied agent in virtual environment
Zhao, Z., Chai, W., Wang, X., Boyi, L., Hao, S., Cao, S., Ye, T., Hwang, J.-N., and Wang, G · 2023
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Minedojo: Building open-ended embodied agents with internet-scale knowledge
Fan, L., Wang, G., Jiang, Y., Mandlekar, A., Yang, Y., Zhu, H., Tang, A., Huang, D.-A., Zhu, Y., and Anandkumar, A · 2022
Cited alongside, same era.
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
Cited alongside, same era.
Significant-gravitas/auto-gpt: An experimental open-source attempt to make gpt-4 fully autonomous., 2023
autogpt · 2023
Cited alongside, same era.
Parameter-efficient fine-tuning of large-scale pre-trained language models
Ding, N., Qin, Y., Yang, G., Wei, F., Yang, Z., Su, Y., Hu, S., Chen, Y., Chan, C.-M., Chen, W., et al · 2023
Cited alongside, same era.
Visual instruction tuning
Liu, H., Li, C., Wu, Q., and Lee, Y. J
Cited in the paper.
Rl-gpt: Integrating reinforcement learning and code-as-policy
Liu, S., Yuan, H., Hu, M., Li, Y., Chen, Y., Liu, S., Lu, Z., and Jia, J
Cited in the paper.
Voyager: An open-ended embodied agent with large language models
Wang, G., Xie, Y., Jiang, Y., Mandlekar, A., Xiao, C., Zhu, Y., Fan, L., and Anandkumar, A
Cited in the paper.
Later among the works it cites.
A survey on evaluation of large language models
Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., et al · 2024
Closest in time.
Llama-rider: Spurring large language models to explore the open world
Feng, Y., Wang, Y., Liu, J., Zheng, S., and Lu, Z · 2024
Closest in time.
Rashidi, A. and Nili Ahmadabadi, M · 2024
Closest in time.
Tinyllava: A framework of small-scale large multimodal models
Zhou, B., Hu, Y., Weng, X., Jia, J., Luo, J., Liu, X., Wu, J., and Huang, L · 2024
Closest in time.