FlexiFly: Interfacing the Physical World with Foundation Models Empowered by Reconfigurable Drone Systems

Foundation models (FM) have shown immense human-like capabilities for generating digital media. However, foundation models that can freely sense, interact, and actuate the physical domain is far from being realized. This is due to 1) requiring dense deployments of sensors to fully cover and analyze large spaces, while 2) events often being localized to small areas, making it difficult for FMs to pinpoint relevant areas of interest relevant to the current task. We propose FlexiFly, a platform that enables FMs to "zoom in" and analyze relevant areas with higher granularity to better understand the physical environment and carry out tasks. FlexiFly accomplishes by introducing 1) a novel image segmentation technique that aids in identifying relevant locations and 2) a modular and reconfigurable sensing and actuation drone platform that FMs can actuate to "zoom in" with relevant sensors and actuators. We demonstrate through real smart home deployments that FlexiFly enables FMs and LLMs to complete diverse tasks up to 85% more successfully. FlexiFly is critical step towards FMs and LLMs that can naturally interface with the physical world.

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Publication Details

Published
2025-05-04
DOI
https://doi.org/10.1145/3715014.3722081
Citations
2
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
2.55

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article

FlexiFly: Interfacing the Physical World with Foundation Models Empowered by Reconfigurable Drone Systems

Minghui Zhao, Kaiyuan Hou, Stephen Xia, Xiaofan Jiang et al.
2 citations
Robotic Path Planning Algorithms
2.55
article

FlexiFly: Interfacing the Physical World with Foundation Models Empowered by Reconfigurable Drone Systems

Minghui Zhao, Kaiyuan Hou, Stephen Xia, Xiaofan Jiang, Junxi Xia, Yanchen Liu
article en
2 citations

Abstract

Foundation models (FM) have shown immense human-like capabilities for generating digital media. However, foundation models that can freely sense, interact, and actuate the physical domain is far from being realized. This is due to 1) requiring dense deployments of sensors to fully cover and analyze large spaces, while 2) events often being localized to small areas, making it difficult for FMs to pinpoint relevant areas of interest relevant to the current task. We propose FlexiFly, a platform that enables FMs to "zoom in" and analyze relevant areas with higher granularity to better understand the physical environment and carry out tasks. FlexiFly accomplishes by introducing 1) a novel image segmentation technique that aids in identifying relevant locations and 2) a modular and reconfigurable sensing and actuation drone platform that FMs can actuate to "zoom in" with relevant sensors and actuators. We demonstrate through real smart home deployments that FlexiFly enables FMs and LLMs to complete diverse tasks up to 85% more successfully. FlexiFly is critical step towards FMs and LLMs that can naturally interface with the physical world.

Northwestern University (US), Columbia University (US)
National Science Foundation
Openalex Percentile: Top 11%
Robotic Path Planning Algorithms
2.55
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