Neuromuscular model achieving speed control and steering with a 3D bipedal walker

Noot, Nicolas Van der; Ijspeert, Auke Jan; Ronsse, Renaud

doi:10.1007/s10514-018-9814-6

Cited by 12 publications

(8 citation statements)

References 51 publications

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“…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.…”

Section: ) Sensory Informationmentioning

confidence: 99%

Artificial Human Balance Control by Calf Muscle Activation Modelling

et al. 2020

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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.INDEX TERMS Muscle stretch reflex, calf muscle activation, standing control, exoskeleton control.

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Section: ) Sensory Informationmentioning

confidence: 99%

Artificial Human Balance Control by Calf Muscle Activation Modelling

et al. 2020

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“…Researchers have used neuromechanical simulations to test gait assistive devices before developing hardware [45,46] and to understand how changes in musculoskeletal properties affect walking performance [26,7]. Moreover, the control model can be directly used to control bipedal robots [47,48,49] and assistive devices [50,45,51].…”

Section: Neuromechanical Control Models and Simulationsmentioning

confidence: 99%

Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation

Song

Kidziński

Peng

et al. 2020

Preprint

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Modeling human motor control and predicting how humans will move in novel environments is a grand scientific challenge. Despite advances in neuroscience techniques, it is still difficult to measure and interpret the activity of the millions of neurons involved in motor control. Thus, 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, which we have run annually since 2017 at the NeurIPS conference, has attracted over 1300 teams from around the world. Top teams adapted state-of-art deep reinforcement learning techniques to produce complex 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.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Finite-time disturbance reconstruction and robust fractional-order controller design for hybrid port-Hamiltonian dynamics of biped robots

Farid,

Ruggiero

2021

Preprint

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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.

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Neuromuscular model achieving speed control and steering with a 3D bipedal walker

Cited by 12 publications

References 51 publications

Artificial Human Balance Control by Calf Muscle Activation Modelling

Artificial Human Balance Control by Calf Muscle Activation Modelling

Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation

Finite-time disturbance reconstruction and robust fractional-order controller design for hybrid port-Hamiltonian dynamics of biped robots

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