The virtual pivot point (VPP), a theoretical convergence point of ground reaction forces during gait, has gained attention for its potential to uncover underlying locomotor control strategies. Here, we present the first investigation of VPP in individuals with unilateral above-knee amputation, using a publicly available dataset of 18 participants. Subjects were categorized into K2 (walking speeds 0.4–0.8m/s) and K3 (0.6–1.4m/s) functional levels. Our findings show that both groups demonstrate high sagittal-plane VPP quality, comparable to that of healthy individuals, with R2> 95%, indicating a strong relationship between VPP formation and sagittal plane dynamics. Conversely, in the frontal plane, VPP analysis reveals greater variability and lower quality, indicating the absence of a well-defined pivot during gait. Notably, frontal-plane VPP quality deteriorates with increasing walking speed, particularly in K3 ambulators. While this speed-dependency is observed in healthy individuals as well, the rate of decline is significantly steeper in amputees. Additionally, spatial analysis of VPP positions reveals a consistent elevation of the amputated leg’s VPP compared to the intact leg. These findings emphasize the importance of frontal plane dynamics in amputee gait and suggest improvements in prosthetic design to enhance control and promote more symmetrical, natural gait.

Human locomotion exhibits extraordinary adaptability and robustness, yet the mechanisms by which lower limbs adjust to sudden environmental disruptions remain poorly understood. To address this, we employed the bioinspired human-sized EPA-Hopper II robot to examine how lower-limb joints recover from an abrupt drop in ground height, mimicking unexpected perturbations encountered in natural settings. Our study investigates the roles of the monoarticular soleus (SOL) and biarticular gastrocnemius (GAS) muscle configurations, focusing on how their compliance influences the robot’s hopping stability. Experiments reveal that a coordinated interplay between SOL and GAS markedly improves recovery from disturbances, enhancing energy distribution and joint synchronization. Detailed kinematic and power analyses show that GAS facilitates energy transfer across joints, while SOL’s spring-like properties support rapid recovery. These results highlight how bioinspired muscle arrangements enable robust locomotion through intrinsic mechanical interactions. By leveraging a robotic platform to probe these dynamics, this work deepens our understanding of biological locomotion and informs the design of bioinspired bipedal robots and prosthetics capable of thriving in unpredictable environments.

Legged robots, particularly quadrupeds, excel at navigating rough terrains, yet their performance under vertical ground perturbations, such as those from oscillating surfaces, remains underexplored. This study introduces a novel approach to enhance quadruped locomotion robustness by training the Unitree Go2 robot on an oscillating bridge—a 13.24-meter steel-and-concrete structure with a 2.0 Hz eigenfrequency designed to perturb locomotion. Using Reinforcement Learning (RL) with the Proximal Policy Optimization (PPO) algorithm in a MuJoCo simulation, we trained 15 distinct locomotion policies, combining five gaits (trot, pace, bound, free, default) with three training conditions: rigid bridge and two oscillating bridge setups with differing height regulation strategies (relative to bridge surface or ground). Domain randomization ensured zero-shot transfer to the real-world bridge. Our results demonstrate that policies trained on the oscillating bridge exhibit superior stability and adaptability compared to those trained on rigid surfaces. Our framework enables robust gait patterns even without prior bridge exposure. These findings highlight the potential of simulation-based RL to improve quadruped locomotion during dynamic ground perturbations, offering insights for designing robots capable of traversing vibrating environments.

The lower limb morphology of biological locomotors is abundant in muscle-tendon units. Yet, not much is known about how these actuation units contribute to the output performance and energy economy of movements. In this work, we investigate the functionality of four of the important lower limb muscles - Vastus, Popliteus, Soleus, and Gastrocnemius - in a hopping task at different frequencies (1.5-3.5 Hz). These muscles are implemented as pneumatic artificial muscles (PAMs) on the EPA-Hopper-II robot, which is a human-sized 3-segmented leg co-actuated by electrical motors and PAMs. A bioinspired reflex-based Force Modulated Control (FMC) is also implemented on the robot to achieve hopping at different frequencies. The results show that the Vastus contributes the most to energy-efficient hopping at low to mid frequencies. The biarticular Gastrocnemius also helps increase efficiency at low frequencies. Further, it is found that the Gastrocnemius synchronizes the knee-ankle motion and mitigates lateral knee motion. The outcomes of this work add further evidence to hypotheses regarding human lower-limb actuation and proper recruitment of muscles for building more efficient robots.

There is a long history demonstrating humans' tendency to create artificial copies of living creatures. For moving machines called robots, actuators play a key role in developing human-like movements. Among different types of actuation, PAMs (pneumatic artificial muscles) are known as the most similar ones to biological muscles. In addition to similarities in force generation mechanism (tension based), the well-accepted argumentation from Klute et al., states that the PAM force-length (fl) behavior is close to biological muscles, while the force-velocity (fv) pattern is different. Using the multiplicative formulation of the pressure (as an activation term), fland fvbeside an additive passive parallel elastic element, we present a new model of PAM. This muscle-based model can predict PAM dynamic behaviors with high precision. With a second experiment on a two-segmented leg, the proposed model is verified to predict the generated forces of PAMs in an antagonistic arrangement. Such a dynamic muscle-like model of artificial muscles can be used for the design and control of legged robots to generate robust, efficient and versatile gaits.