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SYNLOCO-VE: Synthesizing central pattern generator with reinforcement learning and velocity estimator for quadruped locomotion

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OPTIMAL CONTROL APPLICATIONS & METHODS 2025年第2期46卷 493-511页

作者： Zhang, Xinyu Xiao, Zhiyuan Zhou, Xiang Zhang, Qingrui Sun Yat sen Univ Sch Aeronaut & Astronaut Shenzhen Campus Shenzhen 518107 Peoples R China

It is a challenging task to learn a robust and natural locomotion controller for quadruped robots at different terrains and velocities. In particular, the locomotion learning task will be even more difficult for the case with no exteroceptive sensors. In this article, the learning-based locomotion control is, therefore, investigated for quadruped robots only using proprioceptive sensors. A new framework called SYNLOCO-VE is proposed by synthesizing a feedforward gait planner, a trunk velocity estimator, and reinforcement learning (RL). The feedforward gait planner is developed based on the well-known central pattern generator, but it can change the foot length for improved velocity tracking performance. The trunk velocity estimator is designed based on deep learning, which estimates the trunk velocity using historical data from proprioceptive sensors. The introduction of the trunk velocity estimator can mitigate the influence of the partial observation issue due to the lack of exteroceptive sensors. RL is employed to learn a feedback controller to regulate the robot gaits using feedback from proprioceptive sensors and the trunk velocity estimation. In the proposed framework, the feedforward gait planner can also guide the training process of RL, thus resulting in more stable and faster policy learning. Ablation studies are provided to demonstrate the efficiency of different modules in the proposed design. Extensive experiments are performed using a quadruped robot Go1, which only has proprioceptive sensors. The proposed framework is able to learn robust and stable locomotion at different terrains and tasks. Experimental comparisons are also conducted to illustrate the advantages of the proposed design over the state-of-the-art methods. SYNLOCO-VE is a novel framework for learning robust locomotion in quadruped robots using only proprioceptive sensors. By integrating a feedforward gait planner, a trunk velocity estimator, and reinforcement learning, our approach achi

关键词： central pattern generator deep learning quadruped locomotion reinforcement learning velocity estimation

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central pattern generator based reflexive control of quadruped walking robots using a recurrent neural network

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ROBOTICS AND AUTONOMOUS SYSTEMS 2014年第10期62卷 1497-1516页

作者： Duc Trong Tran Koo, Ig Mo Lee, Yoon Haeng Moon, Hyungpil Park, Sangdeok Koo, Ja Choon Choi, Hyouk Ryeol Sungkyunkwan Univ Sch Mech Engn Intelligent Robot & Mechatron Syst Lab Suwon South Korea Korea Inst Ind Technol Div Appl Robot Technol Ansan South Korea

This paper presents a novel central pattern generator (CPG) model for controlling quadruped walking robots. The improvement of this model focuses on generating any desired waveforms along with accurate online modulation. In detail, a well-analyzed Recurrent Neural Network is used as the oscillators to generate simple harmonic periodic signals that exhibit limit cycle effects. Then, an approximate Fourier series is employed to transform those mentioned simple signals into arbitrary desired outputs under the phase constraints of several primary quadruped gaits. With comprehensive closed-form equations, the model also allows the user to modulate the waveform, the frequency and the phase constraint of the outputs online by directly setting the inner parameters without the need for any manual tuning. In addition, an associated controller is designed using leg coordination Cartesian position as the control state space based on which stiffness control is performed at sub-controller level. In addition, several reflex modules are embedded to transform the feedback of all sensors into the CPG space. This helps the CPG recognize external disturbances and utilize inner limit cycle effect to stabilize the robot motion. Finally, experiments with a real quadruped robot named AiDIN III performing several dynamic trotting tasks on several unknown natural terrains are presented to validate the effectiveness of the proposed CPG model and controller. (C) 2014 Elsevier B.V. All rights reserved.

关键词： central pattern generator Quadruped robots Biomimetic control

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central pattern generator and feedforward neural network-based self-adaptive gait control for a crab-like robot locomoting on complex terrain under two reflex mechanisms

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2017年第4期14卷 1-13页

作者： Wang, Gang Chen, Xi Han, Shi-Kai Harbin Engn Univ Coll Shipbldg Engn Harbin Heilongjiang Peoples R China Harbin Engn Univ Sci & Technol Underwater Vehicle Lab Harbin Heilongjiang Peoples R China Heilongjiang Inst Technol Coll Mech & Elect Engn Harbin Heilongjiang Peoples R China Chinese Acad Sci Shenyang Inst Automat Shenyang Liaoning Peoples R China

Although quite a few central pattern generator controllers have been developed to regulate the locomotion of terrestrial bionic robots, few studies have been conducted on the central pattern generator control technique for amphibious robots crawling on complex terrains. The present article proposes a central pattern generator and feedforward neural network-based self-adaptive gait control method for a crab-like robot locomoting on complex terrain under two reflex mechanisms. In detail, two nonlinear ordinary differential equations are presented at first to model a Hopf oscillator with limit cycle effects. Having Hopf oscillators as the basic units, a central pattern generator system is proposed for the waveform-gait control of the crab-like robot. A tri-layer feedforward neural network is then constructed to establish the one-to-one mapping between the central pattern generator rhythmic signals and the joint angles. Based on the central pattern generator system and feedforward neural network, two reflex mechanisms are put forward to realize self-adaptive gait control on complex terrains. Finally, experiments with the crab-like robot are performed to verify the waveform-gait generation and transition performances and the self-adaptive locomotion capability on uneven ground.

关键词： Crab-like robot central pattern generator Hopf oscillator self-adaptive control complex terrains

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central pattern generator network model for the alternating hind limb gait of rats based on the modified Van der Pol equation

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2023年第2期61卷 555-566页

作者： Shen, Xiaoyan Wu, Yan Lou, Xiongjie Li, Zhiling Ma, Lei Bian, Xiongheng Nantong Univ Sch Informat Sci & Technol 9 Seyuan Rd Nantong 226019 Jiangsu Peoples R China Nantong Res Inst Adv Commun Technol Nantong Jiangsu Peoples R China Nantong Univ Coinnovat Ctr Neuroregenerat Nantong Jiangsu Peoples R China

Herein, we employed a central pattern generator (CPG), a spinal cord neural network that regulates lower-limb gait during intra-spinal micro-stimulation (ISMS). Particularly, ISMS was used to determine the spatial distribution pattern of CPG sites in the spinal cord and the signal regulation pattern that induced the CPG network to produce coordinated actions. Based on the oscillation phenomenon of the single CPG neurons of Van der Pol (VDP) oscillators, a double-cell CPG neural network model was constructed to realise double lower limbs, six-joint modelling, the simulation of 12 neural circuits, the CPG loci characterising stimuli-inducing alternating movements and changes in polarity stimulation signals in rat hindlimbs, and leg-state change movements. The feasibility and effectiveness of the CPG neural network were verified by recording the electromyographic burst-release mode of the flexor and extensor muscles of the knee joints during CPG electrical stimulation. The results revealed that the output pattern of the CPG presented stable rhythm and coordination characteristics. The 12-neuron CPG model based on the improved VDP equation realised single-point control while significantly reducing the number of stimulation electrodes in the gait training of spinal cord injury patients. We believe that this study advances motor function recovery in rehabilitation medicine.

关键词： central pattern generator Intra-spinal micro-stimulation Van der Pol oscillation model Alternating movement Spinal cord injury

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central pattern generator Parameter Search for a Biped Walking Robot Using Nonparametric Estimation Based Particle Swarm Optimization

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2009年第3期7卷 447-457页

作者： Kim, Jeong-Jung Lee, Jun-Woo Lee, Ju-Jang Korea Adv Inst Sci & Technol Div Elect Engn Sch Elect Engn & Comp Sci Taejon 350701 South Korea Korea Adv Inst Sci & Technol Robot Program Taejon 350701 South Korea

A parameter search for a central pattern generator (CPG) for biped walking is difficult because there is no methodology to set the parameters and the search space is broad. These characteristics of the parameter search result in numerous fitness evaluations. In this paper, nonparametric estimation based Particle Swarm Optimization (NEPSO) is suggested to effectively search the parameters of CPG. The NEPSO uses a concept experience repository to store a previous position and the fitness of particles in a PSO and estimated best position to accelerate a convergence speed. The proposed method is compared with PSO variants in numerical experiments and is tested in a three dimensional dynamic simulator for bipedal walking. The NEPSO effectively finds CPG parameters that produce a gait of a biped robot. Moreover, NEPSO has a fast convergence property which reduces the evaluation of fitness in a real environment.

关键词： Biped robot central pattern generator particle swarm optimization

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central pattern generator-based coupling control method for synchronously controlling the two-degrees-of-freedom robot

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SCIENCE PROGRESS 2020年第1期103卷 36850419877731-36850419877731页

作者： Fu, Qiang Luo, Tianhong Pan, Chunpeng Wu, Guoguo Chongqing Univ Arts & Sci Dept Robot Engn Chongqing 402160 Peoples R China

Synchronous control is a fundamental and significant problem for controlling a multi-joint robot. In this article, by applying two coupled Rayleigh oscillators as the referred central pattern generator models for the two joints of a two-degrees-of-freedom robot, the central pattern generator-based coupling control method is proposed for controlling the two-degrees-of-freedom robot's joints. On these bases, the co-simulation model of the two-degrees-of-freedom robot with the proposed central pattern generator-based coupling control method is established via ADAMS and MATLAB/Simulink, and the performance of the central pattern generator-based coupling control method on synchronizing two motions of two-degrees-of-freedom robot's joints is numerically simulated. Furthermore, the experimental setup of a two-degrees-of-freedom robot is established based on the real-time simulations system via the proposed central pattern generator-based coupling control method. And experiments are carried out on the established setup. Simulations and experimental results show that the phase of the controlled two-degrees-of-freedom robot's joints is mutual locked to other, and their motion pattern can be adjusted through the coupling parameter in the central pattern generator-based coupling control method. In conclusion, the proposed central pattern generator-based coupling control method can control the two-degrees-of-freedom robot's joints to produce the coordinated motions and adjust the rhythmic pattern of the two-degrees-of-freedom robot.

关键词： Rayleigh oscillator Two-degrees-of-freedom robot central pattern generator synchronous control

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central pattern generator Based Crawl Gait Control for Quadruped Robot

Central Pattern Generator Based Crawl Gait Control for Quadr...

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IEEE International Conference on Information and Automation (ICIA)

作者： Liu, Jianfeng Pu, Jiexin Gu, Jason. Henan Univ Sci & Technol Sch Informat Engn Luoyang Henan Province Peoples R China Dalhousie Univ Fac Engn Halifax NS Canada

ISBN: (纸本)9781509041022

In order to realize the coordinating and stable gait control for quadruped robot, this paper presents a hierarchical distributed controller structure of CPG by studying CPG-based gait control. The two output modes of the CPG network based on Kimura model are compared, and the waveform characteristics under different parameters and its effect in the practical application on gait are analyzed. Using neuron state output model to build the network connection between the CPG and joint oscillator and quadruped robot rhythmic crawl gait is realized with mapping function. Experiment results show that the controller structure can achieve coordinating crawl gait for quadruped robot and has a good control stability.

关键词： Quadruped Robot central pattern generator Gait Controller

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central pattern generator and Its Learning Via Simultaneous Perturbation Method

Central Pattern Generator and Its Learning Via Simultaneous ...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)/International Joint Conference on Neural Networks (IJCNN)/IEEE Congress on Evolutionary Computation (IEEE-CEC)/IEEE World Congress on Computational Intelligence (IEEE-WCCI)

作者： Maeda, Yutaka Ito, Akihiro Ito, Hidetaka Kansai Univ Fac Engn Sci Suita Osaka Japan

ISBN: (纸本)9781467314909

In this paper, we propose models and learning schemes of central pattern generator(CPG). The CPG models consist of plural neural oscillators which generate simple waves. Combining the neural oscillators, the model can generate complicated waveforms. In order for the CPG to generate desired wave, it is important and essential to present a suitable learning scheme. In this paper, learning schemes using the simultaneous perturbation optimization method is introduced. This learning scheme utilizes only output of the CPG. Therefore, unlike the ordinary back-propagation learning rule, the proposed learning scheme is easily applicable to the CPG models. Moreover, complex-valued CPG is also proposed. In the CPG, inputs, outputs and the other variables are basically complex numbers. Learning scheme based on the simultaneous perturbation method is also introduced. Walking motion control for humanoid robot is considered as an example. The CPG could learn and control ten joint angles of the robot for walking and stepping motion patterns. Moreover, three different desired waveforms in real part and imaginary part are considered for the proposed complex-valued CPG.

关键词： central pattern generator simultaneous perturbation learning scheme motion control complex-valued CPG

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central pattern generator Based on Interstitial Cell Models Made from Bursting Neuron Models 25th

Central Pattern Generator Based on Interstitial Cell Models ...

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25th International Conference on Neural Information Processing (ICONIP)

作者： Toizumi, Takahiro Saeki, Katsutoshi Nihon Univ Grad Sch Sci & Technol Chiyoda Ku 1-8-14 Kandasurugadai Tokyo 1018308 Japan Nihon Univ Coll Sci & Technol 7-24-1 Narashinodai Funabashi Chiba 2748501 Japan

ISBN: (纸本)9783030041793;9783030041786

It is well understood that basic locomotion in living organisms is controlled by a central pattern generator (CPG). We previously studied a hardware CPG model based on interstitial cell models. The interstitial cell models can output low-frequency oscillations (approximately 2 to 5 Hz) with a 900-fF capacitor. However, in order to obtain these low frequencies, the signal must cascade through dozens of stages of neuron models, which dramatically increases the mounting area. In this paper, the mounting area was reduced by constructing an interstitial cell model using bursting neuron models area. By using the bursting neuron models, the mounting area was reduced by approximately 73% while obtaining the same frequency. Moreover, the proposed CPG model can generate transitions between quadruped locomotion patterns.

关键词： Bursting neuron model Analog circuit central pattern generator Interstitial cell model

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Control strategy for intraspinal microstimulation based on central pattern generator

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2023年第3期26卷 305-314页

作者： Lou, Xiongjie Wu, Yan Lu, Song Shen, Xiaoyan Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China Nantong Univ Collaborat Innovat Ctr Nerve Regenerat Nantong Peoples R China

Intraspinal microstimulation (ISMS) is considered as a special functional electrical stimulation (FES) method. This method can restore the movement of paralyzed limbs in patients with spinal cord injury (SCI) using electrical stimulation of spinal cord. There is a special site for central pattern generator (CPG) in the spinal cord. The ISMS acts on the CPG site, and single electrode stimulation produces alternating motion in the hindlimbs of SCI rats. Based on the long short-term memory network (LSTM), a mapping model was established between the stimulation intensity of specific CPG sites and the angle of the knee joint to reflect the motor characteristics of the rat hindlimb. We proposed an LSTM-iterative learning control (ILC) strategy to form a closed-loop control to accurately control hindlimb movement. The proposed LSTM model fits the actual joint angle curve well, and the LSTM-ILC strategy can accurately regulate the hindlimb movement, allowing rats to perform rehabilitation training based on pre-set knee trajectories.

关键词： intraspinal microstimulation central pattern generator spinal cord injury functional electrical stimulation

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