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Abstract Current studies on human locomotion focus mainly on solid ground walking conditions. In this paper, we present a biomechanical comparison of human walking locomotion on solid ground and sand. A novel dataset containing three-dimensional motion and biomechanical data from 20 able-bodied adults for walking locomotion on solid ground and sand is collected. We present the data collection methods and report the sensor data along with the kinematic and kinetic profiles of joint biomechanics. The results reveal significant gait adaptations to the yielding terrain (i.e., sand), such as increased stance duration, reduced push-off force, and altered joint angles and moments. Specifically, the knee angle during the gait cycle on sand shows a delayed peak flexion and an increased overall magnitude, highlighting an adaptation to maintain stability on yielding terrain. These adjustments, including changes in joint timing and energy conservation mechanisms, provide insights into the motion control strategies humans adopt to navigate on yielding terrains. The dataset, containing synchronized ground reaction forces (GRFs) and kinematic data, offers a valuable resource for further exploration in foot–terrain interactions and human walking assistive devices development on yielding terrains. 

Abstract External and internal convertible (EIC) form-based motion control is one of the effective designs of simultaneous trajectory tracking and balance for underactuated balance robots. Under certain conditions, the EIC-based control design is shown to lead to uncontrolled robot motion. To overcome this issue, we present a Gaussian process (GP)-based data-driven learning control for underactuated balance robots with the EIC modeling structure. Two GP-based learning controllers are presented by using the EIC property. The partial EIC (PEIC)-based control design partitions the robotic dynamics into a fully actuated subsystem and a reduced-order underactuated subsystem. The null-space EIC (NEIC)-based control compensates for the uncontrolled motion in a subspace, while the other closed-loop dynamics are not affected. Under the PEIC- and NEIC-based, the tracking and balance tasks are guaranteed, and convergence rate and bounded errors are achieved without causing any uncontrolled motion by the original EIC-based control. We validate the results and demonstrate the GP-based learning control design using two inverted pendulum platforms.