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Navigation with Large Language Models: Discussion and References

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In this paper we study how the “semantic guesswork” produced by language models can be utilized as a guiding heuristic for planning algorithms.

This is paper is available on arxiv under CC 4.0 DEED license. Authors: Dhruv Shah, UC Berkeley and he contributed equally; Michael Equi, UC Berkeley and he contributed equally; Blazej Osinski, University of Warsaw; Fei Xia, Google DeepMind; Brian Ichter, Google DeepMind; Sergey Levine, UC Berkeley and Google DeepMind.

Robotics and Automation Letters, 2019. D. Shah, A. Sridhar, A. Bhorkar, N. Hirose, and S. Levine. GNM: A General Navigation Model to Drive Any Robot. In arXiV, 2022. D. Shah, A. Sridhar, N. Dashora, K. Stachowicz, K. Black, N. Hirose, and S. Levine. ViNT: A Foundation Model for Visual Navigation. In 7th Annual Conference on Robot Learning , 2023. 8 S. K. Ramakrishnan, D. S. Chaplot, Z. Al-Halah, J. Malik, and K. Grauman.

Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames. In International Conference on Learning Representations , 2020. 7, 9 D. S. Chaplot, H. Jiang, S. Gupta, and A. Gupta. Semantic curiosity for active visual learning. In ECCV, 2020. 7, 8, 9, 14 B. Yu, H. Kasaei, and M. Cao. L3mvn: Leveraging large language models for visual target navigation, 2023. 7, 9 D. Shah, B. Eysenbach, N. Rhinehart, and S. Levine. Rapid exploration for open-world navigation with latent goal models.

Robotics and Automation Letters, 2019. D. Shah, A. Sridhar, A. Bhorkar, N. Hirose, and S. Levine. GNM: A General Navigation Model to Drive Any Robot. In arXiV, 2022. D. Shah, A. Sridhar, N. Dashora, K. Stachowicz, K. Black, N. Hirose, and S. Levine. ViNT: A Foundation Model for Visual Navigation. In 7th Annual Conference on Robot Learning , 2023. 8 S. K. Ramakrishnan, D. S. Chaplot, Z. Al-Halah, J. Malik, and K. Grauman.

 

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