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.

This study introduces a robust planning framework that utilizes a model predictive control (MPC) approach, enhanced by incorporating signal temporal logic (STL) specifications. This marks the first-ever study to apply STL-guided trajectory optimization for bipedal locomotion, specifically designed to handle both translational and orientational perturbations. Existing recovery strategies often struggle with reasoning complex task logic and evaluating locomotion robustness systematically, making them susceptible to failures caused by inappropriate recovery strategies or lack of robustness. To address these issues, we design an analytical stability metric for bipedal locomotion and quantify this metric using STL specifications, which guide the generation of recovery trajectories to achieve maximum robustness degree. To enable safe and computational-efficient crossed-leg maneuver, we design data-driven self-leg-collision constraints that are 1000 times faster than the traditional inverse-kinematics-based approach. Our framework outperforms a state-of-the-art locomotion controller, a standard MPC without STL, and a linear-temporal-logic-based planner in a high-fidelity dynamic simulation, especially in scenarios involving crossed-leg maneuvers. Additionally, the Cassie bipedal robot achieves robust performance under horizontal and orientational perturbations such as those observed in ship motions. These environments are validated in simulations and deployed on hardware. Furthermore, our proposed method demonstrates versatility on stepping stones and terrain-agnostic features on inclined terrains.

Manual materials handling occupations often involve repetitive lifting, lowering, and carrying motions, which can lead to muscular fatigue and/or injury. The risk increases when loads must be worn on the body for the entirety of a job shift. Exoskeletons have been developed to assist these types of motions, but require the user to bear the weight of a load through their body. Load carriage exoskeletons have been developed to offload worn mass from the user to the ground through the device structure, but they have had limited success and have not been well studied in manual materials handling tasks. In this paper, we introduce a hip-knee-ankle exoskeleton and two control methods: virtual model control and gravity compensation. We compared the ability of each controller to reduce lower-limb muscle activity during squatting. Because the virtual model controller is tailored to squatting, we hypothesized that it would outperform gravity compensation. Both controllers were able to reduce the activity of major lower-limb muscle groups during squatting when compared to squatting with the exoskeleton turned off. Contrary to our original hypothesis, the gravity compensation controller generally outperformed the virtual model controller, which may have been caused by the gravity compensation controller having more consistent knee torque application and the virtual model controller requiring better per-user tuning and familiarization. These results indicate the efficacy of both controllers in reducing injury risk in the lower limbs during squatting.

Mobility, a critical factor in quality of life, is often rehabilitated using simplistic solutions, such as walkers. Exoskeletons (wearable robotics) offer a more sophisticated rehabilitation approach. However, non-adherence to externally worn mobility aids limits their efficacy. Here, we present the concept of a fully implantable assistive limb actuator that overcomes non-adherence constraints, and which can provide high-precision assistive force. In a bipedal animal model (fowl), we have developed a variable length isometric actuator (measuring ϕ9 x 30 mm) that is able to be directly implanted within the leg via a bone anchor and tendon fixation, replacing the lateral gastrocnemius muscle belly. The actuator is able to generate isometric force similar to the in vivo force of the native muscle, designed to generate assistive torque at the ankle and reduce muscular demand at no additional energy cost. The device has a stroke of 10 mm that operates up to 770 mm/s (77 stroke lengths/s), capable of acting as a clutch (disengaging when needed) and with a tunable slack length to modulate the timing and level of assistive force during gait. Surgical techniques to attach the actuator to the biological system, the Achilles tendon and tibia, have been established and validated using survival surgeries and cadaveric specimens.

Fall prevention is one of the most important components in senior care. We present a technique to augment an assistive walking device with the ability to prevent falls. Given an existing walking device, our method develops a fall predictor and a recovery policy by utilizing the onboard sensors and actuators. The key component of our method is a robust human walking policy that models realistic human gait under a moderate level of perturbations. We use this human walking policy to provide training data for the fall predictor, as well as to teach the recovery policy how to best modify the person's gait when a fall is imminent. Our evaluation shows that the human walking policy generates walking sequences similar to those reported in biomechanics literature. Our experiments in simulation show that the augmented assistive device can indeed help recover balance from a variety of external perturbations. We also provide a quantitative method to evaluate the design choices for an assistive device.

Wearable robotics offers a unique opportunity to explore how biological systems interface with engineered parts. But, due to a gap in understanding of the underlying biological mechanisms at work, the state of the art in design and development is a sophisticated form of automated trial and error. Progress is hampered by the difficulty of assessing the direct impact of wearable robots on underlying muscles, tendons and bones during human experimentation. While animal models have provided an experimental platform to explore other biological mechanisms, as of yet, no animal model of a wearable robot during locomotion has been developed. To fill this gap, we have built the first ever wearable robotic device for a freely-Iocomoting, non-human, bipedal animal (Numida melaegris = Guinea fowl), a species whose gait closely mirrors human locomotion mechanics. We found that a spring-loaded soft-exosuit that passively augments the energy stored in distal tendons was both well tolerated and provided consistent torques. Preliminary data showed birds systematically change their kinematics in response to changes to exo-suit spring stiffness, adjusting the timing but not magnitude of the assistive torques. This animal model for wearable robotics allows experiments up and down the broader spatiotemporal scale that are not currently possible in humans. With it we can address questions from short-term adaptations in musculoskeletal dynamics within a single step to broader behavioral and physical changes that come with long term use.