HydroWrench: A Dataset and Benchmark for Dynamic Load Reconstruction in Bioinspired Aquatic Mechanisms

Dynamic loads shape the propulsive performance and structural response of bioinspired aquatic mechanisms, making their reconstruction important for surrogate modeling and motion optimization. We introduce HydroWrench, a dataset and benchmark of 116,756 command–load trajectories from caudal-fin, webbed-foot, and flipper mechanisms representative of added-mass, drag-based, and lift-based propulsion. The dataset provides recorded joint-angle commands and derived kinematics as inputs, with measured six-axis interface loads as targets. With executed motion and flow unobserved, the benchmark compares nonlinear representation and additional command history under a shared evaluation protocol, with models fitted separately for each mechanism. A multilayer perceptron reduces normalized mean squared error by 25.5–81.0% relative to linear regression with matched features. Additional history improves time-varying reconstruction for the webbed foot and flipper, while caudal-fin model rankings depend on the temporal scoring grid. In the webbed-foot high-frequency holdout, a long-history predictor reduces error by 15.1% relative to its matched local-input counterpart, while its error remains 32.3% higher than under in-distribution conditions. These results connect reconstruction accuracy to temporal error structure and motion-condition coverage. HydroWrench provides a shared experimental basis for load-relevant representations, dynamic surrogate modeling, and input–output system identification in partially observed fluid–mechanism systems.