Merge pull request #85 from StanfordASL/mjf-su-patch-1

Update ASL_Bib.bib

_bibliography/ASL_Bib.bib

@@ -4194,6 +4194,19 @@ @inproceedings{FoutterSinhaEtAl2023
   timestamp    = {2024-03-01}
 }
 
+@inproceedings{FoutterBohjEtAl2024,
+  author       = {Foutter, M. and Bhoj, P. and Sinha, R. and Elhafsi, A. and Banerjee, S. and Agia, C. and Kruger, J. and Guffanti, T. and Gammelli, D. and D'Amico, S. and Pavone, M.},
+  title        = {Adapting a Foundation Model for Space-based Tasks},
+  booktitle    = proc_RSS_SemRob,
+  year         = {2024},
+  asl_abstract = {Foundation models, e.g., large language models, possess attributes of intelligence which offer promise to endow a robot with the contextual understanding necessary to navigate complex, unstructured tasks in the wild. In the future of space robotics, we see three core challenges which motivate the use of a foundation model adapted to space-based applications: 1) Scalability of ground-in-the-loop operations; 2) Generalizing prior knowledge to novel environments; and 3) Multi-modality in tasks and sensor data. Therefore, as a first-step towards building a foundation model for space-based applications, we automatically label the AI4Mars dataset to curate a language annotated dataset of visual-question-answer tuples. We fine-tune a pretrained LLaVA checkpoint on this dataset to endow a vision-language model with the ability to perform spatial reasoning and navigation on Mars' surface. In this work, we demonstrate that 1) existing vision-language models are deficient visual reasoners in space-based applications, and 2) fine-tuning a vision-language model on extraterrestrial data significantly improves the quality of responses even with a limited training dataset of only a few thousand samples.},
+  asl_address  = {Delft, Netherlands},
+  asl_url      = {https://arxiv.org/abs/2408.05924},
+  url          = {https://arxiv.org/abs/2408.05924},
+  owner        = {foutter},
+  timestamp    = {2024-08-12}
+}
+
 @inproceedings{FladerAhnEtAl2016,
   author    = {Flader, I. B. and Ahn, C. H. and Gerrard, D. D. and Ng, E. J. and Yang, Y. and Hong, V. A. and Pavone, M. and Kenny, T. W.},
   title     = {Autonomous calibration of {MEMS} disk resonating gyroscope for improved sensor performance},