Vision Language Action (VLA) Models for Unmanned Aerial Robotics and Bimanual Manipulation: A Review
This review synthesizes 183 contributions from 2017-2026 covering VLA architectures, training recipes, action representations, bimanual coordination (2022-2026), UAV navigation and control (2017-2026), language grounding, and cross-cutting concerns. It shows that strategies from bimanual VLAs transfer to aerial systems and identifies fourteen research directions.
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[Submitted on 7 Jul 2026]
Title:Vision Language Action (VLA) Models for Unmanned Aerial Robotics and Bimanual Manipulation: A Review
View a PDF of the paper titled Vision Language Action (VLA) Models for Unmanned Aerial Robotics and Bimanual Manipulation: A Review, by Inkyu Sa and 4 other authors
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Abstract:Vision Language Action (VLA) models unify visual perception, natural-language understanding, and action generation within a single foundation model, allowing a robot to follow instructions such as fold the towel or fly to the red building directly from camera images. Because VLAs inherit world knowledge from internet-scale pre-training, they have become the dominant framework for learning-based manipulation, with bimanual coordination serving as the most demanding testbed: two arms with 7 degrees of freedom each must move in concert to fold, assemble, and reorient objects. Unmanned aerial robotics faces a structurally similar challenge: a drone must coordinate thrust, attitude, and increasingly gripper commands from visual observations under strict latency and payload constraints. This review covers 183 contributions spanning 2017-2026 and organized along seven dimensions: VLA architectures, training recipes, action representations, bimanual coordination (2022-2026), unmanned aerial vehicle (UAV) navigation and control (2017-2026), language grounding, and cross-cutting concerns including memory and world models. We show that the coordination strategies, training recipes, and action representations developed for bimanual VLAs transfer to unmanned aerial systems and identify fourteen research directions across both domains.
Comments: 56 pages, 11 figures, 16 tables
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2607.06706 [cs.RO]
(or arXiv:2607.06706v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.06706
arXiv-issued DOI via DataCite (pending registration)
Related DOI:
https://doi.org/10.3390/drones10060412
DOI(s) linking to related resources
Submission history
From: Inkyu Sa [view email] [v1] Tue, 7 Jul 2026 18:24:09 UTC (1,454 KB)
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