Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

3P-LLM: Probabilistic Path Planning using Large Language Model for Autonomous Robot Navigation (2403.18778v1)

Published 27 Mar 2024 in cs.RO

Abstract: Much worldly semantic knowledge can be encoded in LLMs. Such information could be of great use to robots that want to carry out high-level, temporally extended commands stated in natural language. However, the lack of real-world experience that LLMs have is a key limitation that makes it challenging to use them for decision-making inside a particular embodiment. This research assesses the feasibility of using LLM (GPT-3.5-turbo chatbot by OpenAI) for robotic path planning. The shortcomings of conventional approaches to managing complex environments and developing trustworthy plans for shifting environmental conditions serve as the driving force behind the research. Due to the sophisticated natural language processing abilities of LLM, the capacity to provide effective and adaptive path-planning algorithms in real-time, great accuracy, and few-shot learning capabilities, GPT-3.5-turbo is well suited for path planning in robotics. In numerous simulated scenarios, the research compares the performance of GPT-3.5-turbo with that of state-of-the-art path planners like Rapidly Exploring Random Tree (RRT) and A*. We observed that GPT-3.5-turbo is able to provide real-time path planning feedback to the robot and outperforms its counterparts. This paper establishes the foundation for LLM-powered path planning for robotic systems.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (35)
  1. E. Latif and R. Parasuraman, “Communication-efficient multi-robot exploration using coverage-biased distributed q-learning,” IEEE Robotics and Automation Letters, 2024.
  2. T. Dang, F. Mascarich, S. Khattak, C. Papachristos, and K. Alexis, “Graph-based path planning for autonomous robotic exploration in subterranean environments,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2019, pp. 3105–3112.
  3. E. Latif and R. Parasuraman, “Dgorl: Distributed graph optimization based relative localization of multi-robot systems,” in International Symposium on Distributed Autonomous Robotic Systems.   Springer, 2022, pp. 243–256.
  4. ——, “Gprl: Gaussian processes-based relative localization for multi-robot systems,” arXiv preprint arXiv:2307.10614, 2023.
  5. D. Ferguson, M. Likhachev, and A. Stentz, “A guide to heuristic-based path planning,” in Proceedings of the international workshop on planning under uncertainty for autonomous systems, international conference on automated planning and scheduling (ICAPS), 2005, pp. 9–18.
  6. E. Latif and R. Parasuraman, “Multi-robot synergistic localization in dynamic environments,” in ISR Europe 2022; 54th International Symposium on Robotics.   VDE, 2022, pp. 1–8.
  7. ——, “Seal: Simultaneous exploration and localization for multi-robot systems,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023, pp. 5358–5365.
  8. E. Latif, W. Song, and R. Parasuraman, “Communication-efficient reinforcement learning in swarm robotic networks for maze exploration,” in IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2023, pp. 1–6.
  9. K. Karur, N. Sharma, C. Dharmatti, and J. E. Siegel, “A survey of path planning algorithms for mobile robots,” Vehicles, vol. 3, no. 3, pp. 448–468, 2021.
  10. E. Latif and R. Parasuraman, “Instantaneous wireless robotic node localization using collaborative direction of arrival,” IEEE Internet of Things Journal, 2023.
  11. ——, “On the intersection of computational geometry algorithms with mobile robot path planning,” Algorithms, vol. 16, no. 11, p. 498, 2023.
  12. A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al., “Language models are unsupervised multitask learners,” OpenAI blog, vol. 1, no. 8, p. 9, 2019.
  13. E. Latif and X. Zhai, “Fine-tuning chatgpt for automatic scoring,” Computers and Education: Artificial Intelligence, p. 100210, 2024.
  14. ——, “Automatic scoring of students’ science writing using hybrid neural network,” arXiv preprint arXiv:2312.03752, 2023.
  15. T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al., “Language models are few-shot learners,” Advances in neural information processing systems, vol. 33, pp. 1877–1901, 2020.
  16. E. Latif, X. Zhai, and L. Liu, “Ai gender bias, disparities, and fairness: Does training data matter?” arXiv preprint arXiv:2312.10833, 2023.
  17. E. Latif, G.-G. Lee, K. Neuman, T. Kastorff, and X. Zhai, “G-sciedbert: A contextualized llm for science assessment tasks in german,” arXiv preprint arXiv:2402.06584, 2024.
  18. J. Finnie-Ansley, P. Denny, B. A. Becker, A. Luxton-Reilly, and J. Prather, “The robots are coming: Exploring the implications of openai codex on introductory programming,” in Australasian Computing Education Conference, 2022, pp. 10–19.
  19. E. Latif, L. Fang, P. Ma, and X. Zhai, “Knowledge distillation of llm for education,” arXiv preprint arXiv:2312.15842, 2023.
  20. G.-G. Lee, E. Latif, X. Wu, N. Liu, and X. Zhai, “Applying large language models and chain-of-thought for automatic scoring,” Computers and Education: Artificial Intelligence, p. 100213, 2024.
  21. M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog et al., “Do as i can, not as i say: Grounding language in robotic affordances,” arXiv preprint arXiv:2204.01691, 2022.
  22. G.-G. Lee, E. Latif, L. Shi, and X. Zhai, “Gemini pro defeated by gpt-4v: Evidence from education,” arXiv preprint arXiv:2401.08660, 2023.
  23. G.-G. Lee, L. Shi, E. Latif, Y. Gao, A. Bewersdorf, M. Nyaaba, S. Guo, Z. Wu, Z. Liu, H. Wang et al., “Multimodality of ai for education: Towards artificial general intelligence,” arXiv preprint arXiv:2312.06037, 2023.
  24. X. Cao, X. Zou, C. Jia, M. Chen, and Z. Zeng, “Rrt-based path planning for an intelligent litchi-picking manipulator,” Computers and electronics in agriculture, vol. 156, pp. 105–118, 2019.
  25. P. Sharma, B. Sundaralingam, V. Blukis, C. Paxton, T. Hermans, A. Torralba, J. Andreas, and D. Fox, “Correcting robot plans with natural language feedback,” arXiv preprint arXiv:2204.05186, 2022.
  26. Y.-L. Kuo, B. Katz, and A. Barbu, “Deep compositional robotic planners that follow natural language commands,” in 2020 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2020, pp. 4906–4912.
  27. H. Chen, H. Tan, A. Kuntz, M. Bansal, and R. Alterovitz, “Enabling robots to understand incomplete natural language instructions using commonsense reasoning,” in 2020 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2020, pp. 1963–1969.
  28. M. Fox and D. Long, “Pddl2. 1: An extension to pddl for expressing temporal planning domains,” Journal of artificial intelligence research, vol. 20, pp. 61–124, 2003.
  29. M. Eppe, P. D. Nguyen, and S. Wermter, “From semantics to execution: Integrating action planning with reinforcement learning for robotic causal problem-solving,” Frontiers in Robotics and AI, vol. 6, p. 123, 2019.
  30. R. Paul, A. Barbu, S. Felshin, B. Katz, and N. Roy, “Temporal grounding graphs for language understanding with accrued visual-linguistic context,” arXiv preprint arXiv:1811.06966, 2018.
  31. C. Paxton, Y. Bisk, J. Thomason, A. Byravan, and D. Foxl, “Prospection: Interpretable plans from language by predicting the future,” in 2019 International Conference on Robotics and Automation (ICRA).   IEEE, 2019, pp. 6942–6948.
  32. J. Fu, A. Korattikara, S. Levine, and S. Guadarrama, “From language to goals: Inverse reinforcement learning for vision-based instruction following,” arXiv preprint arXiv:1902.07742, 2019.
  33. V. Blukis, D. Misra, R. A. Knepper, and Y. Artzi, “Mapping navigation instructions to continuous control actions with position-visitation prediction,” in Conference on Robot Learning.   PMLR, 2018, pp. 505–518.
  34. S. Tellex, T. Kollar, S. Dickerson, M. Walter, A. Banerjee, S. Teller, and N. Roy, “Understanding natural language commands for robotic navigation and mobile manipulation,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 25, no. 1, 2011, pp. 1507–1514.
  35. B. Balasuriya, B. Chathuranga, B. Jayasundara, N. Napagoda, S. Kumarawadu, D. Chandima, and A. Jayasekara, “Outdoor robot navigation using gmapping based slam algorithm,” in 2016 moratuwa engineering research conference (mercon).   IEEE, 2016, pp. 403–408.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (1)
  1. Ehsan Latif (36 papers)
Citations (3)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now