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With an increase in the capabilities of generative language models, a growing interest in embodied AI has followed.
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Huang, W., Abbeel, P., Pathak, D., Mordatch, I.: Language models as zero-shot planners: Extracting actionable knowledge for embodied agents. In: International conference on machine learning. pp. 9118–9147. PMLR (2022)
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Ren, T., Liu, S., Zeng, A., Lin, J., Li, K., Cao, H., Chen, J., Huang, X., Chen, Y., Yan, F., Zeng, Z., Zhang, H., Li, F., Yang, J., Li, H., Jiang, Q., Zhang, L.: Grounded sam: Assembling open-world models for diverse visual tasks (2024)
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2022
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2022
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Duan, K., Suen, C.W.K., Zou, Z.: Marc: A multi-agent robots control framework for enhancing reinforcement learning in construction tasks (2023)
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Royce, R., Kaufmann, M., Becktor, J., Moon, S., Carpenter, K., Pak, K., Towler, A., Thakker, R., Khattak, S.: Enabling novel mission operations and interactions with rosa: The robot operating system agent (2024)
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Zhao, H., Chen, H., Yang, F., Liu, N., Deng, H., Cai, H., Wang, S., Yin, D., Du, M.: Explainability for large language models: A survey. ACM Trans. Intell. Syst. Technol. 15
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Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Transactions on Information Systems 43
2025
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