Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/17534
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dc.contributor.authorBalaji, Tarunen_US
dc.contributor.authorVineet, Sunkuen_US
dc.contributor.authorKanhangad, Viveken_US
dc.date.accessioned2025-12-25T10:56:44Z-
dc.date.available2025-12-25T10:56:44Z-
dc.date.issued2025-
dc.identifier.citationSingh, S., Saini, V., Shevtsov, M., & Jha, H. C. (2025). Microbiome-modulated molecular chaperones: Gut-brain control of proteostasis and neurodegenerative risk. In Adv. Protein Chem. Struct. Biol. Academic Press Inc.en_US
dc.identifier.citationScopus. https://doi.org/10.1016/bs.apcsb.2025.11.005en_US
dc.identifier.isbn979-8331598259-
dc.identifier.otherEID(2-s2.0-105024848495)-
dc.identifier.urihttps://dx.doi.org/10.1109/ETAAV66793.2025.11213101-
dc.identifier.urihttps://dspace.iiti.ac.in:8080/jspui/handle/123456789/17534-
dc.description.abstractThe integration of Artificial Intelligence (AI) in drone technology has been proven to have a wide range of applications in the logistics, agriculture, construction, security, and military sectors. Furthermore, rapid advancements in large language models and their effectiveness in recent years have been of particular interest in human-robot interaction. Building on these innovations, we present PromptPilot - a multimodal Large Action Model (mLAM) that enables users to interact with drones to execute complex vision-based tasks such as dynamic object tracking using everyday language. We also develop a drone feed classifier that identifies the required modalities of a prompt with a precision of 97.58%, demonstrating strong performance in understanding and executing user queries autonomously. © 2025 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.subjectAutonomous UAVen_US
dc.subjectLLMen_US
dc.subjectMultimodal Large Action Modelen_US
dc.subjectObject Trackingen_US
dc.subjectVLMen_US
dc.titlePromptPilot: Controlling UAVs Using Multimodal AIen_US
dc.typeConference Paperen_US
Appears in Collections:Department of Electrical Engineering

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