Fetching the paper…
Reading the bibliography…
This research focuses on how Large Language Models (LLMs) can help with (path) planning for mobile embodied agents such as robots, in a human-in-the-loop and interactive manner.
2013
Earlier work this paper cites.
P. Wang, Q. Zhang, and Z. Chen, “A grey probability measure set based mobile robot position estimation algorithm,” International Journal of Control, Automation and Systems , vol. 13, pp. 978–985, 2015
2015
Earlier work this paper cites.
D. González, J. Pérez, V. Milanés, and F. Nashashibi, “A review of motion planning techniques for automated vehicles,” IEEE Transactions on intelligent transportation systems , vol. 17, no. 4, pp. 1135–1145, 2015
2015
Earlier work this paper cites.
P. Anderson, Q. Wu, D. Teney, J. Bruce, M. Johnson, N. Sünderhauf, I. Reid, S. Gould, and A. Van Den Hengel, “Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 3674–3683
2018
Earlier work this paper cites.
Q. Zhang, P. Wang, and Z. Chen, “An improved particle filter for mobile robot localization based on particle swarm optimization,” Expert Systems with Applications , vol. 135, pp. 181–193, 2019
2019
Earlier work this paper cites.
M. Andrychowicz, A. Raichuk, P. Stańczyk, M. Orsini, S. Girgin, R. Marinier, L. Hussenot, M. Geist, O. Pietquin, M. Michalski, et al. , “What matters for on-policy deep actor-critic methods? a large-scale study,” in International conference on learning representations , 2020
2020
Earlier work this paper cites.
P. Wang, L. Mihaylova, P. Bonnifait, P. Xu, and J. Jiang, “Feature-refined box particle filtering for autonomous vehicle localisation with openstreetmap,” Engineering Applications of Artificial Intelligence , vol. 105, p. 104445, 2021
2021
Cited alongside, same era.
R. Yonetani, T. Taniai, M. Barekatain, M. Nishimura, and A. Kanezaki, “Path planning using neural a* search,” in International Conference on Machine Learning . PMLR, 2021, pp. 12 029–12 039
2021
Cited alongside, same era.
2022
Cited alongside, same era.
C. Zhou, B. Huang, and P. Fränti, “A review of motion planning algorithms for intelligent robots,” Journal of Intelligent Manufacturing , vol. 33, no. 2, pp. 387–424, 2022
2022
Cited alongside, same era.
OpenAI, “ChatGPT: Engaging openai’s conversational ai,” https://openai.com/blog/chatgpt , 2023, [Accessed on November 20, 2023]
2023
Closest in time.
C. H. Song, J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y. Su, “Llm-planner: Few-shot grounded planning for embodied agents with large language models,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 2998–3009
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2022
Cited alongside, same era.
2023
Closest in time.