LLM Granularity for On-the-Fly Robot Control

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A. Broad, J. Arkin, N. Ratliff, T. Howard, B. Argall, and D. C. Graph, “Towards real-time natural language corrections for assistive robots,” in RSS Workshop on Model Learning for Human-Robot Communication , 2016
2016
Earlier work this paper cites.
C. Carr, P. Wang, and S. Wang, “A human-friendly verbal communication platform for multi-robot systems: Design and principles,” in UK Workshop on Computational Intelligence . Springer, 2023, pp. 580–594
2023
Earlier work this paper cites.
D. Patel, H. Eghbalzadeh, N. Kamra, M. L. Iuzzolino, U. Jain, and R. Desai, “Pretrained language models as visual planners for human assistance,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 15 302–15 314
2023
Earlier work this paper cites.

Y. Cui, S. Karamcheti, R. Palleti, N. Shivakumar, P. Liang, and D. Sadigh, “No, to the right: Online language corrections for robotic manipulation via shared autonomy,” in Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction , 2023, pp. 93–101
2023
Cited alongside, same era.
C. Lynch, A. Wahid, J. Tompson, T. Ding, J. Betker, R. Baruch, T. Armstrong, and P. Florence, “Interactive language: Talking to robots in real time,” IEEE Robotics and Automation Letters , 2023
Original
2023
Cited alongside, same era.
Z. Shi, E. Landrum, A. O’Connell, M. Kian, L. Pinto-Alva, K. Shrestha, X. Zhu, and M. J. Matarić, “How can large language models enable better socially assistive human-robot interaction: A brief survey,” in Proceedings of the AAAI Symposium Series , vol. 3, no. 1, 2024, pp. 401–404
2024
Cited alongside, same era.

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LLM Granularity for On-the-Fly Robot Control · Around

A. Broad, J. Arkin, N. Ratliff, T. Howard, B. Argall, and D. C. Graph, “Towards real-time natural language corrections for assistive robots,” in RSS Workshop on Model Learning for Human-Robot Communication , 2016
2016
Earlier work this paper cites.
C. Carr, P. Wang, and S. Wang, “A human-friendly verbal communication platform for multi-robot systems: Design and principles,” in UK Workshop on Computational Intelligence . Springer, 2023, pp. 580–594
2023
Earlier work this paper cites.
D. Patel, H. Eghbalzadeh, N. Kamra, M. L. Iuzzolino, U. Jain, and R. Desai, “Pretrained language models as visual planners for human assistance,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 15 302–15 314
2023
Earlier work this paper cites.

Y. Cui, S. Karamcheti, R. Palleti, N. Shivakumar, P. Liang, and D. Sadigh, “No, to the right: Online language corrections for robotic manipulation via shared autonomy,” in Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction , 2023, pp. 93–101
2023
Cited alongside, same era.
C. Lynch, A. Wahid, J. Tompson, T. Ding, J. Betker, R. Baruch, T. Armstrong, and P. Florence, “Interactive language: Talking to robots in real time,” IEEE Robotics and Automation Letters , 2023
Original
2023
Cited alongside, same era.
Z. Shi, E. Landrum, A. O’Connell, M. Kian, L. Pinto-Alva, K. Shrestha, X. Zhu, and M. J. Matarić, “How can large language models enable better socially assistive human-robot interaction: A brief survey,” in Proceedings of the AAAI Symposium Series , vol. 3, no. 1, 2024, pp. 401–404
2024
Cited alongside, same era.

alphaXiv searches the wider corpus for related work and actual follow-ups.

alphaXiv is searching for related work…