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![Time-evolution of the 14 neurons firing rates of Fig. 3 over one gait cycle (0%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0\%$$\end{document} and 100%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$100\%$$\end{document} correspond to consecutive right foot strikes, the dashed line corresponds to the left foot strike in-between). These signals are obtained during one typical gait cycle of the locomotion resulting from the controller used in all the results of this paper (called reference controller), with a speed reference of 0.65m/s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.65~\hbox {m}/\hbox {s}$$\end{document}](publication/328431440/figure/fig7/AS:961325400342543@1606209244482/Time-evolution-of-the-14-neurons-firing-rates-of-Fig3-over-one-gait-cycle.png)
Time-evolution of the 14 neurons firing rates of Fig. 3 over one gait cycle (0%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0\%$$\end{document} and 100%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$100\%$$\end{document} correspond to consecutive right foot strikes, the dashed line corresponds to the left foot strike in-between). These signals are obtained during one typical gait cycle of the locomotion resulting from the controller used in all the results of this paper (called reference controller), with a speed reference of 0.65m/s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.65~\hbox {m}/\hbox {s}$$\end{document}
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![Receiving a speed reference vref\documentclass[12pt]{minimal}...](publication/328431440/figure/fig2/AS:961325396135971@1606209243776/Receiving-a-speed-reference-vrefdocumentclass12ptminimal-usepackageamsmath_Q320.jpg)
Nowadays, very few humanoid robots manage to travel in our daily environments. This is mainly due to their limited locomotion capabilities, far from the human ones. Recently, we developed a bio-inspired torque-based controller recruiting virtual muscles driven by reflexes and a central pattern generator. Straight walking experiments were obtained i...
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... Neuromusculoskeletal (NMS) models can provide inspiration for robot controller design and provide the necessary interface to explore the applicability of human strategies to robotic systems. While originally developed to study muscle-driven systems, they are equally well suited for the control of torque-actuated robots [18][19][20][21][22][23][24] . For example, an NMS model can be used to calculate desired torques given joint state information 23,25 . ...
Walking on unknown and rough terrain is challenging for (bipedal) robots, while humans naturally cope with perturbations. Therefore, human strategies serve as an excellent inspiration to improve the robustness of robotic systems. Neuromusculoskeletal (NMS) models provide the necessary interface for the validation and transfer of human control strategies. Reflexes play a crucial part during normal locomotion and especially in the face of perturbations, and provide a simple, transferable, and bio-inspired control scheme. Current reflex-based NMS models are not robust to unexpected perturbations. Therefore, in this work, we propose a bio-inspired improvement of a widely used NMS walking model. In humans, different muscles show an increase in activation in anticipation of the landing at the end of the swing phase. This preactivation is not integrated in the used reflex-based walking model. We integrate this activation by adding an additional feedback loop and show that the landing is adapted and the robustness to unexpected step-down perturbations is markedly improved (from 3 to 10 cm). Scrutinizing the effect, we find that the stabilizing effect is caused by changed knee kinematics. Preactivation, therefore, acts as an accommodation strategy to cope with unexpected step-down perturbations, not requiring any detection of the perturbation. Our results indicate that such preactivation can potentially enable a bipedal system to react adequately to upcoming unexpected perturbations and is hence an effective adaptation of reflexes to cope with rough terrain. Preactivation can be ported to robots by leveraging the reflex-control scheme and improves the robustness to step-down perturbation without the need to detect the perturbation. Alternatively, the stabilizing mechanism can also be added in an anticipatory fashion by applying an additional knee torque to the contralateral knee.
... For instance, the seminal work of Geyer and Herr [7] reported such a model that can generate autonomous bipedal locomotion with reflex-driven activations of virtual muscletendon units. Other researchers extended this model to 3D walking with steering maneuvers [8] and other locomotion tasks like running [9], while it has also been translated to locomotion assistance devices [10]. In [5], [11], we developed a similar neuromusculoskeletal framework, yet with another principle for computing virtual muscle stimulations, namely the use of motor primitives. ...
Lower-limb exoskeletons are robotic devices that can provide assistance to human locomotion.
Since they are expected to be used in ecological environments, their control strategy should handle different kinds of daily-life situations.
Taking inspiration from the human neuromuscular system – and particularly from the so-called motor primitives – may help in adapting the type of delivered assistance to different locomotion tasks.
In this work, we validated the combination of simplified primitives and a musculoskeletal model for assisting healthy subjects with a hip exoskeleton.
This framework showed adaptation to the user’s gait for different slope inclinations, although its effects on the subject’s speed and their perceived effort showed no significant improvement compared to wearing the device in transparent mode.
... Neuromechanical simulations have been used to test gait assistive devices before developing hardware [53,54] and to understand how changes in musculoskeletal properties affect walking performance [7,28]. Moreover, the control model implemented in neuromechanical simulations can be directly used to control bipedal robots [55][56][57] and assistive devices [53,58,59]. ...
Modeling human motor control and predicting how humans will move in novel environments is a grand scientific challenge. Researchers in the fields of biomechanics and motor control have proposed and evaluated motor control models via neuromechanical simulations, which produce physically correct motions of a musculoskeletal model. Typically, researchers have developed control models that encode physiologically plausible motor control hypotheses and compared the resulting simulation behaviors to measurable human motion data. While such plausible control models were able to simulate and explain many basic locomotion behaviors (e.g. walking, running, and climbing stairs), modeling higher layer controls (e.g. processing environment cues, planning long-term motion strategies, and coordinating basic motor skills to navigate in dynamic and complex environments) remains a challenge. Recent advances in deep reinforcement learning lay a foundation for modeling these complex control processes and controlling a diverse repertoire of human movement; however, reinforcement learning has been rarely applied in neuromechanical simulation to model human control. In this paper, we review the current state of neuromechanical simulations, along with the fundamentals of reinforcement learning, as it applies to human locomotion. We also present a scientific competition and accompanying software platform, which we have organized to accelerate the use of reinforcement learning in neuromechanical simulations. This “Learn to Move” competition was an official competition at the NeurIPS conference from 2017 to 2019 and attracted over 1300 teams from around the world. Top teams adapted state-of-the-art deep reinforcement learning techniques and produced motions, such as quick turning and walk-to-stand transitions, that have not been demonstrated before in neuromechanical simulations without utilizing reference motion data. We close with a discussion of future opportunities at the intersection of human movement simulation and reinforcement learning and our plans to extend the Learn to Move competition to further facilitate interdisciplinary collaboration in modeling human motor control for biomechanics and rehabilitation research
... To achieve this goal, it is worth studying humans' outstand- 5 ing features in-depth, such as robustness, dexterity, and adaptability in different environmental conditions. In the last decade, numerous control algorithms were proposed for the motion control and the stabilization of the legged robots [6,7,8,9,10,11,12,13]. Due to the intrinsic structure complexity, the nonlinearity problems, the existence of discrete dynamics caused by environmental impacts, and the undesired effects of external disturbances, the biped robots' dynamic control is still challenging. ...
In this paper, disturbance reconstruction and robust trajectory tracking control of biped robots with hybrid dynamics in the port-Hamiltonian form is investigated. A new type of Hamiltonian function is introduced, which ensures the finite-time stability of the closed-loop system. The proposed control system consists of two loops: an inner and an outer loop. A fractional proportional–integral–derivative filter is used to achieve finite-time convergence for position tracking errors at the outer loop. A fractional-order sliding mode controller acts as a centralized controller at the inner-loop, ensuring the finite-time stability of the velocity tracking error. In this loop, the undesired effects of unknown external disturbance and parameter uncertainties are compensated using estimators. Two disturbance estimators are envisioned. The former is designed using fractional calculus. The latter is an adaptive estimator, and it is constructed using the general dynamic of biped robots. Stability analysis shows that the closed-loop system is finite-time stable in both contact-less and impact phases. Simulation studies on three types of biped robots (i.e., two-link walker, RABBIT biped robot, and flat-feet biped robot) demonstrate the proposed controller’s tracking performance and disturbance rejection capability.
... To achieve this goal, it is worth studying humans' outstanding features indepth, such as robustness, dexterity, and adaptability in different environmental conditions. In the last decade, numerous control algorithms were proposed for the motion control and the stabilization of biped robots [6,7,8,9,10,11,12,13]. Due to the intrinsic structure complexity, the nonlinearity problems, the existence of discrete dynamics caused by environmental impacts, and the undesired effects of external disturbances, the biped robots' dynamic control is still challenging. ...
In this paper, disturbance reconstruction and robust trajectory tracking control of biped robots with hybrid dynamics in the port-Hamiltonian form is investigated. A new type of Hamiltonian function is introduced, which ensures the finite-time stability of the closed-loop system. The proposed control system consists of two loops: an inner and an outer loop. A fractional proportional-integral-derivative filter is used to achieve finite-time convergence for position tracking errors at the outer loop. A fractional-order sliding mode controller acts as a centralized controller at the inner-loop, ensuring the finite-time stability of the velocity tracking error. In this loop, the undesired effects of unknown external disturbance and parameter uncertainties are compensated using estimators. Two disturbance estimators are envisioned. The former is designed using fractional calculus. The latter is an adaptive estimator, and it is constructed using the general dynamic of biped robots. Stability analysis shows that the closed-loop system is finite-time stable in both contact-less and impact phases. Simulation studies on two types of biped robots (i.e., two-link walker and RABBIT biped robot) demonstrate the proposed controller's tracking performance and disturbance rejection capability.
... Achieving adaptive, energy-efficient, and robust locomotion and dealing with asymmetrical conditions for bipedal robots are a challenging problem and have not been fully addressed. Many control techniques have been developed to govern bipedal robots either with the whole body (torso with upper and lower limbs) or the partial parts as lower limbs [1,2,3,4]. The techniques can be classified into two main approaches: (i) the engineeringbased control approach, and (ii) the bio-inspired control approach. ...
The achievement of adaptive, stable, and robust locomotion and dealing with asymmetrical conditions for bipedal robots remain a challenging problem. To address the problem, this paper introduces adaptive parallel reflex- and decoupled central pattern generator (CPG)-based control for a planar bipedal robot. The control has modular structure consisting of two parallel modules that work together. Firstly, as the main controller, the reflex-based control module inspired by an agonist–antagonist model, utilizes proprioceptive sensory feedback to adaptively generate various stable gaits. In parallel, as an auxiliary controller, the decoupled CPG-based control units individually governing the robot legs have the ability to learn the generated gaits in an online manner. Using the proposed framework, our study shows that this real-time control approach contributes to stable gait generation with robustness toward sensory feedback malfunction and adaptability to deal with environmental and morphological changes. Herein this study, we demonstrate the planar bipedal robot control functionality on a variable speed treadmill, dealing with asymmetric conditions such as weight imbalance and asymmetrical elastic resistance in the legs. However, the approach does not require robot kinematic and dynamic models as well as an environmental model and is therefore flexible. As such, it can be used as a basis for controlling other bipedal locomotion systems, like lower-limb exoskeletons.
... We incremented this model with the inclusion of central pattern generators [7] to obtain modulation capabilities, first in 2D [8], and later in 3D environments [9]. Finally, we extended it to control the robot speed and steering (direction and curvature) by the modulation of two simple scalar inputs: the forward speed and heading references [10]. ...
... This paper uses the COMAN platform, a 95 cm tall humanoid robot, as embodiment. As reported in [9] and [10], we use the Robotran simulator to model the robot in its environment. The robot's 23 joints are torque-controlled; these torque references come from virtual muscles, fed by virtual reflexes and a central pattern generator. ...
... In [9] and [10], we developed a bio-inspired controller capable of generating human-like gaits with steering capabilities. More precisely, two high-level inputs, namely the speed (i.e. ...
... The sensory information is motivated by the signals based on the muscle spindle length change and its contraction velocity [22]. This sensory information can be computed using the human ankle angle θ foot , which is defined as the angle between the foot and the shank segment [23], as illustrated in Fig. 1(b). ...
The natural neuromuscular model has greatly inspired the development of control mechanisms in addressing the uncertainty challenges in robotic systems. Although the underpinning neural reaction of posture control remains unknown, recent studies suggest that muscle activation driven by the nervous system plays a key role in human postural responses to environmental disturbance. Given that the human calf is mainly formed by two muscles, this paper presents an integrated calf control model with the two comprising components representing the activations of the two calf muscles. The contributions of each component towards the artificial control of the calf are determined by their weights, which are carefully designed to simulate the natural biological calf. The proposed calf modelling has also been applied to robotic ankle exoskeleton control. The proposed work was validated and evaluated by both biological and engineering simulation approaches, and the experimental results revealed that the proposed model successfully performed over 92% of the muscle activation naturally made by human participants, and the actions led by the simulated ankle exoskeleton wearers were overall consistent with that by the natural biological response.
... An interesting work of Van der Noot et al. [83] uses virtual muscles to calculate robot's joint torques. ...
My thesis aims to simulate the impact of motor disorders on the human gait to help non-invasive diagnosis of neurodegenerative diseases such as Parkinson's disease. Indeed, the simulation of the human locomotor system helps to deepen our understanding of the functioning of the human body by providing biological, biomechanical and kinematic data that would be difficult to collect otherwise and by helping to evaluate the coordination of a patient's movements to predict its condition after surgery. The goal of my thesis is, more specifically, to create a new platform for neuro-musculoskeletal simulation of the human locomotor system to reproduce healthy or altered walking gaits by Parkinson's disease or by disorders of the musculoskeletal system or locomotor disorders. The work presented includes several matters. Firstly, the main principles of the nervous system that control human locomotion are reviewed, by focusing on neural structures located in the brain and which are the sources of parkinsonian disorders. The neural controller of the simulation platform is based on an original model of central pattern generator (CPG) inspired by the spinal locomotor network and developed at LORIA in recent years. The musculoskeletal simulators are used in this thesis to obtain a closed-loop physical simulation of the locomotor system walking on the ground and whose proprioceptive and exteroceptive sensory feedback is used by the CPGs. The musculoskeletal simulator GAIT2DE was used with the OpenSim simulator which is more realistic and more used in Biomechanics field. The simulated gait analysis and controller parameter optimization are concerned followed by the results obtained with the simulators. These results show that it is possible to generate different walking patterns that are relatively stable and coordinated by modifying the neuronal parameters of GPCs. The simulation platform will allow to simulate abnormal gait due to different causes such as neurodegenerative diseases or the impact of the addition of artificial limbs (prostheses) and surgical interventions.
Animal locomotion is the result of complex and multi-layered interactions between the nervous system, the musculo-skeletal system and the environment. Decoding the underlying mechanisms requires an integrative approach. Comparative experimental biology has allowed researchers to study the underlying components and some of their interactions across diverse animals. These studies have shown that locomotor neural circuits are distributed in the spinal cord, the midbrain and higher brain regions in vertebrates. The spinal cord plays a key role in locomotor control because it contains central pattern generators (CPGs) - systems of coupled neuronal oscillators that provide coordinated rhythmic control of muscle activation that can be viewed as feedforward controllers - and multiple reflex loops that provide feedback mechanisms. These circuits are activated and modulated by descending pathways from the brain. The relative contributions of CPGs, feedback loops and descending modulation, and how these vary between species and locomotor conditions, remain poorly understood. Robots and neuromechanical simulations can complement experimental approaches by testing specific hypotheses and performing what-if scenarios. This Review will give an overview of key knowledge gained from comparative vertebrate experiments, and insights obtained from neuromechanical simulations and robotic approaches. We suggest that the roles of CPGs, feedback loops and descending modulation vary among animals depending on body size, intrinsic mechanical stability, time required to reach locomotor maturity and speed effects. We also hypothesize that distal joints rely more on feedback control compared with proximal joints. Finally, we highlight important opportunities to address fundamental biological questions through continued collaboration between experimentalists and engineers.
