Robotic Gait Research Articles

Efficient Anytime CLF Reactive Planning System for a Bipedal Robot on Undulating Terrain

We propose and experimentally demonstrate a reactive planning system for bipedal robots on unexplored, challenging terrain. The system includes: a multilayer local map for assessing traversability; an anytime omnidirectional control Lyapunov function for use with a rapidly exploring random tree star (RRT*) that generates a vector field for specifying motion between nodes; a subgoal finder when the final goal is outside of the current map; and a finite-state machine to handle high-level mission decisions. The system also includes a reactive thread that copes with robot deviations via a vector field, defined by a closed-loop feedback policy. The vector field provides real-time control commands to the robot's gait controller as a function of instantaneous robot pose. The system is evaluated on various challenging outdoor terrains and cluttered indoor scenes in both simulation and experiment on Cassie Blue, a bipedal robot with 20 degrees of freedom. All implementations are coded in C++ with the robot operating system and are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/UMich-BipedLab/CLF_reactive_planning_system</uri> .

Design and analysis of a multi-DOF compliant gait rehabilitation robot

Robot-assisted rehabilitation is a rapidly advancing field whereby robotic devices are used for the treatment of disabilities. Mechanism design and actuation of these devices play important roles in the treatment and the non-ergonomic designs may even increase the cardiorespiratory load and cause discomfort leading to further gait disorders. This article identifies crucial design and actuation aspects of gait rehabilitation robots and presents the first intrinsically compliant gait robot design with three actuated and five passive degrees of freedom, alleviating the prevalent issues. The gait robot joints remain aligned with the human anatomical joints during the swing and the stance phases due to the use of special bushes and dampers. The intrinsically compliant actuators on the new gait robot make it safe to work with human subjects. Increased degrees of freedom allow for natural walking dynamics instead of canceling or constraining them. The robot firmware, including the kinematic modeling, is designed using multiple adaptive neuro-fuzzy inference system to save on the computational time of the robot controller. The new robot design is finally validated for its intended use in gait rehabilitation, by employing a fuzzy logic controller and evaluating the position, velocity, and acceleration profiles of its joints.

Emerging Gaits for a Quadrupedal Template Model With Segmented Legs

Abstract Energy-efficient gaits in walking robots can be obtained by designing elastic systems that exhibit naturally emerging locomotion patterns. Biological legged locomotion serves as inspiration, as animals use different gaits to move at certain speeds while minimizing energy consumption. To understand the underlying dynamics of biological locomotion, simplified models have been proposed. The most common one, the SLIP (spring loaded inverted pendulum) model, can explain the effect of the radial elasticity of linear legs and helps to explain locomotion patterns, especially for running behaviors, in different legged systems. However, the SLIP model is inappropriate for the study of stability of limit cycles in systems with articulated legs, which are most commonly used in real robots. This paper introduces a novel quadrupedal template model featuring articulated elastic legs, non-constant leg stiffness, and dynamic leg swing. Numerical simulation with a continuation approach is used to discover the gaits emerging from the natural dynamics of the model, without imposing any contact sequence a priori. The stability of those gaits is also characterized, in order to facilitate the exploitation of the natural model dynamics for generating locomotion patterns for quadrupedal robots.

Feasibility of an Intelligent Algorithm Based on an Assist-as-Needed Controller for a Robot-Aided Gait Trainer (Lokomat) in Neurological Disorders: A Longitudinal Pilot Study.

Most robotic gait assisted devices are designed to provide constant assistance during the training without taking into account each patient's functional ability. The Lokomat offers an assist-as-needed control via the integrated exercise "Adaptive Gait Support" (AGS), which adapts the robotic support based on the patient's abilities. The aims of this study were to examine the feasibility and characteristics of the AGS during long-term application. Ten patients suffering from neurological diseases underwent an 8-week Lokomat training with the AGS. They additionally performed conventional walking tests and a robotic force measurement. The difference between robotic support during adaptive and conventional training and the relationship between the robotic assessment and the conventional walking and force tests were examined. The results show that AGS is feasible during long-term application in a heterogeneous population. The support during AGS training in most of the gait phases was significantly lower than during conventional Lokomat training. A relationship between the robotic support level determined by the AGS and conventional walking tests was revealed. Moreover, combining the isometric force data and AGS data could divide patients into clusters, based on their ability to generate high forces and their level of motor control. AGS shows a high potential in assessing patients' walking ability, as well as in providing challenging training, e.g., by automatically adjusting the robotic support throughout the whole gait cycle and enabling training at lower robotic support.

Combined Reinforcement Learning and CPG Algorithm to Generate Terrain-Adaptive Gait of Hexapod Robots

Terrain adaptation research can significantly improve the motion performance of hexapod robots. In this paper, we propose a method that combines reinforcement learning with a central pattern generator (CPG) to enhance the terrain adaptation of hexapod robots in terms of gait planning. The hexapod robot’s complex task presents a high-dimensional observation and action space, which makes it challenging to directly apply reinforcement learning to robot control. Therefore, we utilize the CPG algorithm to generate the rhythmic gait while compressing the action space dimension of the agent. Additionally, the proposed method requires less internal sensor information, which exhibits strong applicability. Finally, we conduct experiments and deploy the proposed framework in the simulation environment. The results show that the terrain adaptation policy trained in our framework enables the hexapod robot to move more smoothly and efficiently on rugged terrain compared to the traditional CPG method.

A randomized, cross-over trial comparing the effect of innovative robotic gait training and functional clinical therapy in children with cerebral palsy; a protocol to test feasibility.

Robotic gait training is relatively new in the world of pediatric rehabilitation. Preliminary feasibility studies and case reports include stationary robot-assisted treadmill training. Mobile robotic gait trainers hold greater promise for intensive practice-based therapy within hospitals, schools, rehabilitation centers, and at-home therapy as they enable participation and social integration while practicing high-quality gait patterns. This paper (clinical trials registry number: NCT05378243) provides a detailed description of a mixed-method cross-over trial design with a broad set of outcome measures. Ultimately the goal is to establish the feasibility of this design which includes the collection of qualitative data regarding patient, family, and therapist experience and quantitative data regarding gait efficiency and quality, impact on tone, individualized goal achievement and bone strength.

Towards Generation and Transition of Diverse Gaits for Quadrupedal Robots Based on Trajectory Optimization and Whole-Body Impedance Control

Trajectory optimization (TO) combined with whole-body control (WBC) have been a widely accepted approach for dynamic gait control of quadruped robots. However, there are still open issues in this framework, one is the lack of a unified description of intrinsic inter-limb coordination for wide range of gaits and their transitions in TO, another is motion compliance against disturbances arisen from transitions while maintaining accurate tracking performance. In this letter, we introduce the reduced antero-posterior sequence (APS) gait parametrization approach into the model predictive control (MPC) based TO. The APS gait parametrization, which is enforced as equality constraints in optimization model, enables the representation of diverse gaits, symmetrical and asymmetrical, with five parameters and offers an intuitive way to efficiently implement gait transitions with linear interpolation of gait parameters. We also construct a whole-body impedance controller that integrates the operational impedance controller into WBC, allowing to compliantly track the optimized torso state trajectories and contact forces. The effectiveness of the proposed approach is verified in simulations and experiments. The results indicate that the test robot is able to robustly locomote with various gaits, including diagonal walk, trot, bound, flying trot and traverse gallop at a variety of speeds, and smoothly implement gait transitions among these gaits. Compared to existing gait transition approach, ours can obtain better reference tracking performance and higher energy efficiency during gait transitions.

A comparison of an evolvable hardware controller with an artificial neural network used for evolving the gait of a hexapod robot

This paper investigates the implementation of a novel evolvable hardware controller used in evolutionary robotics. The evolvable hardware consists of a Cartesian based array of logic blocks comprised of multiplexers and logic elements. The logic blocks are configured by a bit stream which is evolved using a genetic algorithm. A comparison is performed between an evolvable hardware and an artificial neural network controller evolved using the same genetic algorithm to produce the gait of a hexapod robot. To compare the two controllers, differences in their evolutionary efficiency and robot performance are investigated. The evolutionary efficiency is measured by the required number of generations to achieve an optimal fitness. An optimal hexapod controller allows the robot to walk forward in a straight line maintaining a constant heading and body attitude. It was found that the evolutionary efficiency and performance of the evolvable hardware and artificial neural network were similar, however the EHW was the most evolutionary efficient requiring less generations on average to evolve. Both evolved controllers were evaluated in simulation, and on a physical robot using a softcore processor and custom hardware implemented on a FPGA. The implementation showed that the controllers performed equally well when deployed, allowing the hexapod to meet the optimal gait criteria. These findings have shown that the evolvable hardware controller is a valid option for robotic control of a multilegged robot such as a hexapod as its evolutionary efficiency and deployed performance on a real robot is comparable to that of an artificial neural network. One future application of these evolvable controllers is in fault tolerance where the robot can dynamically adapt to a fault by evolving the controller to adjust to the fault conditions.

Modulation of corticospinal excitability related to the forearm muscle during robot-assisted stepping in humans

In recent years, the neural control mechanisms of the arms and legs during human bipedal walking have been clarified. Rhythmic leg stepping leads to suppression of monosynaptic reflex excitability in forearm muscles. However, it is unknown whether and how corticospinal excitability of the forearm muscle is modulated during leg stepping. The purpose of the present study was to investigate the excitability of the corticospinal tract in the forearm muscle during passive and voluntary stepping. To compare the neural effects on corticospinal excitability to those on monosynaptic reflex excitability, the present study also assessed the excitability of the H-reflex in the forearm muscle during both types of stepping. A robotic gait orthosis was used to produce leg stepping movements similar to those of normal walking. Motor evoked potentials (MEPs) and H-reflexes were evoked in the flexor carpi radialis (FCR) muscle during passive and voluntary stepping. The results showed that FCR MEP amplitudes were significantly enhanced during the mid-stance and terminal-swing phases of voluntary stepping, while there was no significant difference between the phases during passive stepping. Conversely, the FCR H-reflex was suppressed during both voluntary and passive stepping, compared to the standing condition. The present results demonstrated that voluntary commands to leg muscles, combined with somatosensory inputs, may facilitate corticospinal excitability in the forearm muscle, and that somatosensory inputs during walking play a major role in monosynaptic reflex suppression in forearm muscle.

Diagnosis of psychogenic (functional) gait disorders

Psychogenic gait is common in patients with medically unexplained neurological symptoms and provides significant challenges to healthcare providers. Clinicians may arrive at a correct diagnosis earlier if distinctive positive signs are identified and acknowledged. Psychogenic disorders of posture and gait are common and are the major manifestation in 8–10 % of patients with psychogenic movement disorders. Psychogenic movement disorders can present with varied phenomenology that may resemble organic movement disorders. The diagnosis is based on clinical evaluation with a supporting history and classic features on neurologic examination. In functional gait disorders, walking is often bizarre and does not conform to any of the usual patterns observed with neurologic gait disorders. Astasia-abasia, an inability to stand (astasia) or walk (abasia) in the absence of other neurologic abnormalities, was the term applied by investigators in the mid to late 19thcentury to describe certain patients with a frankly functional gait. Other descriptive terms include gaits that resemble walking on ice, walking a sticky surface, walking through water (bringing to mind excessive slowness), tightrope walking, habitual limping, and bizarre, robotic, knock-kneed, trepidant, anxious, and cautious gaits. Ancillary testing, such as imaging and neurophysiologic studies, can provide supplementary information but is not necessary for diagnosis.

Gait Generation Method of Snake Robot Based on Main Characteristic Curve Fitting.

Gait generation method is one of the important contents of snake robot motion control. Different gait generation methods produce completely different forms of control functions, so snake robots need more complicated programming logic and processes to realize various gaits and their transformation. Therefore, we propose a new unified expression of gait method, The MCC (main characteristics control) method simplifies and unifies the control functions of different snake robots gaits by extracting the main features of the backbone curves of snake robots gaits. Since all periodic curves that meet the Dirichlet conditions can be formed by superposition of sinusoidal curves, taking the "lowest frequency" part that reflects the main characteristics of the curve as the target configuration can simplify the motion control function of snake robots' gaits. Based on the MCC method, some snake robot gaits are reconstructed, including serpentine gait, rolling gait, helix rolling gait, and crawler gait. In addition, based on MCC method, an AEH-sidewinding gait control method is proposed. The backbone of the AEH-sidewinding gait is closer to the ideal elliptic helix, thus improving the accuracy of its kinematics modeling of snake robot sidewinding gait. Finally, the validity of this gait is verified by experiments. This unified gait expression of snake robots will be helpful to realize smooth gait switching between different gaits of snake robots.

Priming locomotor training with transspinal stimulation in people with spinal cord injury: study protocol of a randomized clinical trial

BackgroundThe seemingly simple tasks of standing and walking require continuous integration of complex spinal reflex circuits between descending motor commands and ascending sensory inputs. Spinal cord injury greatly impairs standing and walking ability, but both improve with locomotor training. However, even after multiple locomotor training sessions, abnormal muscle activity and coordination persist. Thus, locomotor training alone cannot fully optimize the neuronal plasticity required to strengthen the synapses connecting the brain, spinal cord, and local circuits and potentiate neuronal activity based on need. Transcutaneous spinal cord (transspinal) stimulation alters motoneuron excitability over multiple segments by bringing motoneurons closer to threshold, a prerequisite for effectively promoting spinal locomotor network neuromodulation and strengthening neural connectivity of the injured human spinal cord. Importantly, whether concurrent treatment with transspinal stimulation and locomotor training maximizes motor recovery after spinal cord injury is unknown.MethodsForty-five individuals with chronic spinal cord injury are receiving 40 sessions of robotic gait training primed with 30 Hz transspinal stimulation at the Thoracic 10 vertebral level. Participants are randomized to receive 30 min of active or sham transspinal stimulation during standing or active transspinal stimulation while supine followed by 30 min of robotic gait training. Over the course of locomotor training, the body weight support, treadmill speed, and leg guidance force are adjusted as needed for each participant based on absence of knee buckling during the stance phase and toe dragging during the swing phase. At baseline and after completion of all therapeutic sessions, neurophysiological recordings registering corticospinal and spinal neural excitability changes along with clinical assessment measures of standing and walking, and autonomic function via questionnaires regarding bowel, bladder, and sexual function are taken.DiscussionThe results of this mechanistic randomized clinical trial will demonstrate that tonic transspinal stimulation strengthens corticomotoneuronal connectivity and dynamic neuromodulation through posture-dependent corticospinal and spinal neuroplasticity. We anticipate that this mechanistic clinical trial will greatly impact clinical practice because, in real-world clinical settings, noninvasive transspinal stimulation can be more easily and widely implemented than invasive epidural stimulation. Additionally, by applying multiple interventions to accelerate motor recovery, we are employing a treatment regimen that reflects a true clinical approach.Trial registrationClinicalTrials.gov NCT04807764. Registered on March 19, 2021.

Robot-Assisted Gait Training with Trexo Home: Users, Usage and Initial Impacts

Robotic gait training has the potential to improve secondary health conditions for people with severe neurological impairment. The purpose of this study was to describe who is using the Trexo robotic gait trainer, how much training is achieved in the home and community, and what impacts are observed after the initial month of use. In this prospective observational single-cohort study, parent-reported questionnaires were collected pre- and post-training. Of the 70 participants, the median age was 7 years (range 2 to 24), 83% had CP, and 95% did not walk for mobility. Users trained 2-5 times/week. After the initial month, families reported a significant reduction in sleep disturbance (p = 0.0066). Changes in bowel function, positive affect, and physical activity were not statistically significant. These findings suggest that families with children who have significant mobility impairments can use a robotic gait trainer frequently in a community setting and that sleep significantly improves within the first month of use. This intervention holds promise as a novel strategy to impact multi-modal impairments for this population. Future work should include an experimental study design over a longer training period to begin to understand the relationship between training volume and its full potential.

Effects of Stroke Rehabilitation Using Gait Robot-Assisted Training and Person-Centered Goal Setting: A Single Blinded Pilot Study

Many stroke survivors have difficulties due to the mobility and activities required in daily living. A walking impairment negatively affects the independent lifestyle of stroke patients, requiring intensive post-stroke rehabilitation. Therefore, the purpose of this study was to examine the effects of stroke rehabilitation using gait robot-assisted training and person-centered goal setting on mobility, the activities of daily living, stroke self-efficacy, and health-related QoL in stroke patients with hemiplegia. An assessor-blinded quasi-experimental study with a pre-posttest nonequivalent control group was used. Participants who were admitted to the hospital with a gait robot-assisted training system were assigned to the experimental group, and those without gait robots were assigned to the control group. Sixty stroke patients with hemiplegia from two hospitals specialized in post-stroke rehabilitation participated. Stroke rehabilitation using gait robot-assisted training and person-centered goal setting for stroke patients with hemiplegia was conducted for a total of six weeks. There were significant differences between the experimental group and control group in the Functional Ambulation Category (t = 2.89, p = 0.005), balance (t = 3.73, p < 0.001), Timed Up and Go (t = -2.27, p = 0.027), Korean Modified Barthel Index (t = 2.58, p = 0.012), 10 m Walking test (t = -2.27, p = 0.040), stroke self-efficacy (t = 2.23, p = 0.030), and health-related quality of life (t = 4.90, p < 0.001). A gait robot-assisted rehabilitation using goal setting for stroke patients with hemiplegia improved gait ability, balance ability, stroke self-efficacy, and health-related quality of life in stroke patients.

Trot Gait Stability Control of Small Quadruped Robot Based on MPC and ZMP Methods

The stability of a quadruped robot is mainly affected by the obstacles in the horizontal direction and the roughness in the vertical direction, which often leads to the robot unable to achieve the desired gait effect. In order to solve this problem, the Model Predictive Control (MPC) model and the Zero Moment Point (ZMP) method are combined, and applied to gait planning and the foot end landing control of a small quadruped robot. The tort gait of a small quadruped robot is the focus of research in this study, which simulated trajectory planning and gait stability. In addition, through comparative analysis with the corresponding experiments, the results show that the simulation results are similar to the experimental results, and the quadruped robot gait is stable. Meanwhile, it shows that the combination of the MPC model and ZMP method is feasible for gait stability control of a quadruped robot.

Robotic versus Conventional Overground Gait Training in Subacute Stroke Survivors: A Multicenter Controlled Clinical Trial.

Although stroke survivors can benefit from robotic gait rehabilitation, stationary robot-assisted gait training needs further investigation. In this paper, we investigated the efficacy of this approach (with an exoskeleton or an end-effector robot) in comparison to the conventional overground gait training in subacute stroke survivors. In a multicenter controlled clinical trial, 89 subacute stroke survivors conducted twenty sessions of robot-assisted gait training (Robotic Group) or overground gait training (Control Group) in addition to the standard daily therapy. The robotic training was performed with an exoskeleton (RobotEXO-group) or an end-effector (RobotEND-group). Clinical outcomes were assessed before (T0) and after (T1) the treatment. The walking speed during the 10-Meter Walk Test (10 MWT) was the primary outcome of this study, and secondary outcomes were the 6-Minute Walk Test (6 MWT), Timed Up and Go test (TUG), and the modified Barthel Index (mBI). The main characteristics assessed in the Robotic and Control groups did not differ at baseline. A significant benefit was detected from the 10 MWT in the Robotic Group at the end of the study period (primary endpoint). A benefit was also observed from the following parameters: 6 MWT, TUG, and mBI. Moreover, patients belonging to the Robot Group outperformed the Control Group in gait speed, endurance, balance, and ADL. The RobotEND-group improved their walking speed more than the RobotEXO-group. The stationary robot-assisted training improved walking ability better than the conventional training in subacute stroke survivors. These results suggest that people with subacute stroke may benefit from Robot-Assisted training in potentiating gait speed and endurance. Our results also support that end-effector robots would be superior to exoskeleton robots for improving gait speed enhancement.

Balance Rehabilitation through Robot-Assisted Gait Training in Post-Stroke Patients: A Systematic Review and Meta-Analysis.

Balance impairment is a common disability in post-stroke survivors, leading to reduced mobility and increased fall risk. Robotic gait training (RAGT) is largely used, along with traditional training. There is, however, no strong evidence about RAGT superiority, especially on balance. This study aims to determine RAGT efficacy on balance of post-stroke survivors. PubMed, Cochrane Library, and PeDRO databases were investigated. Randomized clinical trials evaluating RAGT efficacy on post-stroke survivor balance with Berg Balance Scale (BBS) or Timed Up and Go test (TUG) were searched. Meta-regression analyses were performed, considering weekly sessions, single-session duration, and robotic device used. A total of 18 trials have been included. BBS pre-post treatment mean difference is higher in RAGT-treated patients, with a pMD of 2.17 (95% CI 0.79; 3.55). TUG pre-post mean difference is in favor of RAGT, but not statistically, with a pMD of -0.62 (95%CI - 3.66; 2.43). Meta-regression analyses showed no relevant association, except for TUG and treatment duration (β = -1.019, 95% CI - 1.827; -0.210, p-value = 0.0135). RAGT efficacy is equal to traditional therapy, while the combination of the two seems to lead to better outcomes than each individually performed. Robot-assisted balance training should be the focus of experimentation in the following years, given the great results in the first available trials. Given the massive heterogeneity of included patients, trials with more strict inclusion criteria (especially time from stroke) must be performed to finally define if and when RAGT is superior to traditional therapy.

Effects of Timed Frontal Plane Pelvic Moments During Overground Walking With a Mobile TPAD System.

Robotic gait training may improve overground ambulation for individuals with poor control over pelvic motion. However, there is a need for an overground gait training robotic device that allows full control of pelvic movement and synchronizes applied forces to the user's gait. This work evaluates an overground robotic gait trainer that applies synchronized forces on the user's pelvis, the mobile Tethered Pelvic Assist Device. To illustrate one possible control scheme, we apply assistive frontal plane pelvic moments synchronized with the user's continuous gait in real-time. Ten healthy adults walked with the robotic device, with and without frontal plane moments. The frontal plane moments corresponded to 10% of the user's body weight with a moment arm of half their pelvic width. The frontal plane moments significantly increased the range of frontal plane pelvic angles from 2.6° to 9.9° and the sagittal and transverse planes from 4.6° to 10.1° and 3.0° to 8.3°, respectively. The frontal plane moments also significantly increased the activation of the left gluteus medius muscle, which assists in regulating pelvic obliquity. The right gluteus medius muscle activation did not significantly differ when frontal plane moments were applied. This work highlights the ability of the mobile Tethered Pelvic Assist Device to apply a continuous pelvic moment that is synchronized with the user's gait cycle. This capability could change how overground robotic gait training strategies are designed and applied. The potential for gait training interventions that target gait deficits or muscle weakness can now be explored with the mobile Tethered Pelvic Assist Device.

Gait Training with Robotic Exoskeleton Assisted Rehabilitation System in Patients with Incomplete Traumatic and Non-Traumatic Spinal Cord Injury

This pilot study aimed to assess the safety and feasibility of robotic gait training and its' effects on gait parameters in individuals with incomplete motor spinal cord injury-SCI (AIS C and AIS D). The study was conducted in a tertiary research center with indigenously developed Robotic Exoskeleton Assisted Rehabilitation Systems (REARS). Primary outcome measures used were the ten-meter walk test (10MWT), two-minute walk test (2MWT), six-minute walk test (6MWT), the timed up and go test (TUG), the walking index for spinal cord injury II (WISCI II), and the spinal cord independence measure version III (SCIM III) at baseline, 12 sessions, and after 24 sessions (endpoint) of training. At baseline, individuals who could not perform 10MWT, TUG, and 6MWT were grouped in G1 for analysis. Participants in G2 were able to perform all the tests at baseline. The median (interquartile range [IQR]) age and duration of illness was 41 (24) years and 167 (147) days, respectively. Five out of seven participants had non-traumatic etiology and five were males. After completing training, participants in G1 were able to complete the 10MWT, 6MWT, and TUG, and the mean (SD) scores were 0.2 m/s (0.2), 66.3 m (61.2) and 113.3 s (117.4), respectively. Participants in G2 could perform the TUG test 13.5 s faster at the end of the study (11.9 s vs 25.4 s). The minimum clinically important difference (MCID) for TUG was 10.8 s. In G2, the pre-post training change in mean score of 10MWT and 6MWT was 0.11 m/s and 42 m, respectively; these values approached the MCID for these measures. None of the participants had any injury during training. Robotic gait training with REARS is safe and feasible. Such training may lead to an improvement in balance and walking capacity.

Trening hoda uz pomoć robotizovanih trenažera hoda kod osoba nakon moždanog udara

A gait disorder, which arose as a result of a stroke, leads to a significant disability. The main goal of neurorehabilitation is to restore the function of independent movement through conventional physiotherapy, but also the application of Robot-assisted Gait Training. The aim of this paper is to analyze the current use of robotic gait trainers in the rehabilitation of gait in people after a stroke. In clinical practice, there are different types of devices that are adapted for people, both in the subacute and chronic stages after suffering a stroke. Changes in gait function resulting from the use of robotic gait trainers, such as increases in gait speed, stride length, and spatial symmetry, are evident in clinical practice. However, there is a lack of follow-up evaluations and long-term effects, as well as risk assessment of the use of these devices in gait rehabilitation in people after a stroke.