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Semantic anomaly detection with large language models
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“While these methods advance the cognitive capabilities of embodied AI, most still rely on manual prompting to structure their reasoning processes, limiting their autonomous adaptation and generalization.”
From Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning (2025) · cites this paper as Elhafsi et al., 2023
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“Alternative to direct OOD classification, other methods seek to improve the often poorly calibrated uncertainty scores of neural networks on OOD data [30].”
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PaLM-E: An Embodied Multimodal Language Model
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Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items
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C2AE: Class Conditioned Auto-Encoder for Open-Set Recognition
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“LLMs have been used for various purposes such as autonomous navigation (Shah et al., 2023), long-horizon planning (Brohan et al., 2023; Huang et al., 2022; Lin et al., 2023) and policy synthesis (Liang et al., 2022).”
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Harnessing Large Language Models for Training-Free Video Anomaly Detection
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Metadata
- Authors
- Elhafsi, A., Sinha, R., Agia, C., Schmerling, E., Nesnas, I. A. D., & Pavone, M.
- Affiliations
- Stanford University (US)
- Jet Propulsion Laboratory (US)
- Corresponding author
- Elhafsi, Amine
- Venue
- Autonomous Robots
- Published
- 2023-10-23
- Volume
- 47
- Issue
- 8
- Pages
- 1035-1055
- ISSN
- 0929-5593 (ISSN-L)
- 1573-7527
- OpenAlex
- W4387876242
- Cited by
- 101
- References
- 57
- Citations by year
- 2026: 25 · 2025: 46 · 2024: 29 · 2023: 1
- FWCI
- 9.52
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Autonomous Robots (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Aeronautics and Space Administration (80NSSC19K1143, 80NSSC20M0163)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- semantic anomaly detection, large language models, autonomous driving, object manipulation, anomaly monitoring, foundation models, semantic reasoning
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