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Robotics researchers increasingly leverage large language models (LLM) in robotics systems, using them as interfaces to receive task commands, generate task plans, form team coalitions, and allocate tasks among multi-robot and human agents.
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2024
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X. Wu, R. Xian, T. Guan, J. Liang, S. Chakraborty, F. Liu, B. M. Sadler, D. Manocha, and A. Bedi, “On the safety concerns of deploying llms/vlms in robotics: Highlighting the risks and vulnerabilities,” in First Vision and Language for Autonomous Driving and Robotics Workshop , 2024
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S. Izquierdo-Badiola, G. Canal, C. Rizzo, and G. Alenyà, “Plancollabnl: Leveraging large language models for adaptive plan generation in human-robot collaboration,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 17 344–17 350
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Z. Mandi, S. Jain, and S. Song, “Roco: Dialectic multi-robot collaboration with large language models,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 286–299
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2024
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2024
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2024
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I. Obi, R. Pant, S. S. Agrawal, M. Ghazanfar, and A. Basiletti, “Value Imprint: A Technique for Auditing the Human Values Embedded in RLHF Datasets,” in The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track
Cited in the paper.
Z. Yuan, Z. Xiong, Y. Zeng, N. Yu, R. Jia, D. Song, and B. Li, “RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content,” in Forty-first International Conference on Machine Learning
Cited in the paper.
K. Obata, T. Aoki, T. Horii, T. Taniguchi, and T. Nagai, “Lip-LLM: Integrating Linear Programming and dependency graph with Large Language Models for multi-robot task planning,” IEEE Robotics and Automation Letters , 2024
2024
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