Due to hemiparesis, stroke survivors frequently develop a dysfunctional gait that is often characterized by an overall decrease in walking speed and a unilateral decrease in step length. With millions currently affected by this dysfunctional gait, robust and effective rehabilitation protocols are needed. Although robotic devices have been used in numerous rehabilitation protocols for gait, the lack of significant aftereffects that translate to effective therapy makes their application still questionable. This paper proposes a novel type of robot-assisted intervention that results in significant aftereffects that last much longer than any other previous study. With the utilization of a novel robotic device, the Variable Stiffness Treadmill (VST), the stiffness of the walking surface underneath one leg is decreased for a number of steps. This unilateral stiffness perturbation results in a significant aftereffect that is both useful for stroke rehabilitation and often lasts for over 200 gait cycles after the intervention has concluded. More specifically, the aftereffect created is an increase in both left and right step lengths, with the unperturbed step length increasing significantly more than the perturbed. These effects may be helpful in correcting two of the most common issues in post-stroke gait: overall decrease in walking speed and a unilateral shortened step length. The results of this work show that a robot-assisted therapy protocol involving repeated unilateral stiffness perturbations can lead to a more permanent and effective solution to post-stroke gait.

Abstract-- Foot drop is commonly managed using Functional Electrical Stimulation (FES), typically delivered via open-loop controllers with fixed stimulation intensities. While users may manually adjust the intensity through external controls, this approach risks overstimulation, leading to muscle fatigue and discomfort, or understimulation, which compromises dorsiflexion and increases fall risk. In this study, we propose a novel closed-loop FES controller that dynamically adjusts the stimulation intensity based on real-time toe clearance, providing "assistance as needed". We evaluate this system by inducing foot drop in healthy participants and comparing the effects of the closed-loop controller with a traditional open-loop controller across various walking conditions, including different speeds and surface inclinations. Kinematic data reveal that our closed-loop controller maintains adequate toe clearance without significantly affecting the joint angles of the hips, the knees, and the ankles, and while using significantly lower stimulation intensities compared to the open-loop controller . These findings suggest that the proposed method not only matches the effectiveness of existing systems but also offers the potential for reduced muscle fatigue and improved long-term user comfort and adherence.

We introduce a novel musculoskeletal model of a dog, procedurally generated from accurate 3D muscle meshes. Accompanying this model is a motion capture-based locomotion task compatible with a variety of control algorithms, as well as an improved muscle dynamics model designed to enhance convergence in differentiable control frameworks. We validate our approach by comparing simulated muscle activation patterns with experimentally obtained electromyography (EMG) data from previous canine locomotion studies. This work aims to bridge gaps between biomechanics, robotics, and computational neuroscience, offering a robust platform for researchers investigating muscle actuation and neuromuscular control.We plan to release the full model along with the retargeted motion capture clips to facilitate further research and development.

This work presents the design, build, control, and preliminary user data of a locomotion interface called ForceBot. It delivers lower-body haptic interaction in virtual reality (VR), enabling users to walk in VR while interacting with various simulated terrains. It utilizes two planar gantries to give each foot two degrees of freedom and passive heel-lifting motion. The design used motion capture data with dynamic simulation for ergonomic human-robot workspace and hardware selection. Its system framework uses open-source robotic software and pairs with a custom-built power delivery system that offers EtherCAT communication with a 1,000 Hz soft real-time computation rate. This system features an admittance controller to regulate physical human-robot interaction (pHRI) alongside a walking algorithm to generate walking motion and simulate virtual terrains. The system's performance is explored through three measurements that evaluate the relationship between user input force and output pHRI motion. Overall, this platform presents a unique approach by utilizing planar gantries to realize VR terrain interaction with an extensive workspace, reasonably compact footprint, and preliminary user data.

Animals achieve robust locomotion by offloading regulation from the brain to physical couplings within the body. Contrarily, locomotion in artificial systems often depends on centralized processors. Here, we introduce a rapid and autonomous locomotion strategy with synchronized gaits emerging through physical interactions between self-oscillating limbs and the environment, without control signals. Each limb is a single soft tube that only requires constant flow of air to perform cyclic stepping motions at frequencies reaching 300 hertz. By combining several of these self-oscillating limbs, their physical synchronization enables tethered and untethered locomotion speeds that are orders of magnitude faster than comparable state-of-the-art. We demonstrate that these seemingly simple devices exhibit autonomy, including obstacle avoidance and phototaxis, opening up avenues for robust and functional robots at all scales.

While many advancements have been made in the development of template models for describing upright-trunk locomotion, the majority of the effort has been focused on the stance phase. In this paper, we develop a new compact dynamic model as a first step toward a fully unified locomotion template model (ULT-model) of an upright-trunk forward hopping system, which will also require a unified control law in the next step. We demonstrate that all locomotion subfunctions are enabled by adding just a point foot mass and a parallel leg actuator to the well-known trunk SLIP model and that a stable limit cycle can be achieved. This brings us closer toward the ultimate goal of enabling closed-loop dynamics for anchor matching and thus achieving simple, efficient, robust and stable upright-trunk gait control, as observed in biological systems.

McGeer [1] developed a simple bipedal robot with passive legs attached to the hip; this robot could walk stably without the need for any energy input other than gravity by descending a slope. The aforementioned study indicated that passive locomotion can play a significant role in gait. However, the dynamical mechanisms under such locomotion is complex to fully understand sorely from observation. To overcome the limitations of observational approach, several researchers have investigated gait mechanisms using simple walking models [2-7]. Bipedal locomotion consists of not only walking but also running. Therefore, some researchers further developed simple running models. For example, the spring-loaded inverted pendulum (SLIP) model, comprising a point mass and prismatic massless spring, can effectively reproduce the dynamics of running [8-12]. So far, to realize the running motion, we developed a bipedal robot that utilizes bouncing rod dynamics [13]. Recently, we developed a bipedal robot with actu-CONTACT Tomoya Kamimura.

This paper focuses on the analysis of human gait cycle dynamics and presents a mathematical model to determine the torque exerted on the lower limb joints throughout the complete gait cycle, including its various phases. The study involved a healthy subject who participated in a series of initial walking experiments. The development of a mathematical model that accurately represents the natural motion of the human lower limb has garnered significant attention in the field of lower limb prosthetics design. In this study, the researchers incorporated the functional relationship between the limb joints and the end effector of the lower extremity. This knowledge is crucial for rehabilitation purposes as it helps in understanding the connectivity of joints, links, and the overall body orientation required to effectively control the motion of the actuators. When analysing physical activities, measurements of human strength play a crucial role. Traditionally, these measurements have focused on determining the maximum voluntary torque at a single joint angle and angular velocity. However, it is important to consider that the available strength varies significantly with joint position and velocity.

Entanglements like vines and branches in natural settings or cords and pipes in human spaces prevent mobile robots from accessing many environments. Legged robots should be effective in these settings, and more so than wheeled or tracked platforms, but naive controllers quickly become entangled and stuck. In this paper we present a method for proprioception aimed specifically at the task of sensing entanglements of a robot's legs as well as a reaction strategy to disentangle legs during their swing phase as they advance to their next foothold. We demonstrate our proprioception and reaction strategy enables traversal of entanglements of many stiffnesses and geometries succeeding in 14 out of 16 trials in laboratory tests, as well as a natural outdoor environment.

The problem of designing and stabilizing impact-free, energy-conserving gaits is considered for underactuated, point-foot planar bipeds. Virtual holonomic constraints are used to design energy-conserving gaits. A desired gait corresponds to a periodic hybrid orbit and is stabilized using the Impulse Controlled Poincar\'e Map approach. Numerical simulations for the case of a five-link biped demonstrate convergence to a desired gait from arbitrary initial conditions.