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 186 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.

UAVs increasingly rely on accurate SLAM for aerial mapping and inspection in GPS-denied environments. 3D Gaussian Splatting (3DGS) has opened a new direction for UAV mapping by allowing SLAM systems to build dense, photorealistic, and renderable maps. Yet in 3DGS SLAM the map is optimized from the pose graph, so a false loop closure can deform both the UAV trajectory and the Gaussian map consumed by downstream UAV autonomy. Reliable loop admission is therefore relevant to safe GPS-denied operation because it protects the state and map estimates on which autonomous functions depend. The present work evaluated this upstream estimation-integrity problem; it did not measure closed-loop guidance, control, or navigation-safety outcomes. We address the loop-admission problem that arises after a place-recognition (PR) module proposes a candidate loop and relative-pose seed. GSSeq is a rendered-reference sequential verifier that uses the current Gaussian map as active evidence before inserting a loop factor. It renders RGB-D references with the PR seed, checks LiDAR/rendered-depth consistency and image/rendered-reference consistency over active support, and propagates the seed through a short query trajectory window. A loop is admitted only when this evidence remains geometrically supported and photometrically stable. On fixed LiDAR-PR candidate sets spanning MARS-LVIG, MUN-FRL, and independent NTU-VIRAL aerial sequences together with ground-mobility benchmarks, GSSeq provides a competitive precision-oriented operating point while suppressing false loop admissions. Thresholds calibrated only on NTU-VIRAL spms_01 combine rendered RGB agreement with LiDAR-submap geometry and are then frozen for spms_02. On this held-out sequence, GSSeq rejects all seven false-positive BTC factors while retaining one of three true-positive factors. The trajectory-to-map experiment reduced ATE RMSE from $2.609\mathrm{m}$ to $1.417\mathrm{m}$ and improved selected-view PSNR from $13.80\mathrm{dB}$ to $16.46\mathrm{dB}$. These results show that rendered verification can preserve an aligned, renderable UAV trajectory-map pair before unsupported loop factors reshape the SLAM map.

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.