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This review synthesizes 183 contributions to Vision–Language–Action (VLA) models, focusing on their application in bimanual manipulation and unmanned aerial robotics. It highlights how the coordination strategies and training methodologies developed for bimanual tasks can be effectively adapted for UAV navigation and control, addressing the complex requirements of both domains. Key findings reveal fourteen promising research directions that leverage the synergies between these two fields, underscoring the potential for cross-domain advancements in robotic capabilities.
Coordination strategies for bimanual manipulation can be directly applied to enhance unmanned aerial vehicle performance, revealing a surprising synergy between these seemingly disparate domains.
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.