Real time implementation of CTRNN and BPTT algorithm to learn on-line biped robot balance: Experiments on the standing posture

Hénaff, Patrick; Scesa, Vincent; Ouezdou, Fethi Ben; Bruneau, Olivier

doi:10.1016/j.conengprac.2010.10.002

Cited by 14 publications

(4 citation statements)

References 45 publications

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“…At the same time, RNN based motion controllers can provide biped robot gait control solutions, which do not have the disadvantages of CPG such as limited motion range and manual tuning requirement, in cases where other analytical-based exact and approximated solutions are not desired or cannot be applied, see e.g. [39,40]. Unlike the CPG parameter tuning case, there are welldefined methods to adjust the parameters of NNs, which are generally called as learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Two-Legged Robot Motion Control With Recurrent Neural Networks

Catalbas

Morgül

2022

J Intell Robot Syst

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Legged locomotion is a desirable ability for robotic systems thanks to its agile mobility and wide range of motions that it provides. In this paper, the use of neural network-based nonlinear controller structures which consist of recurrent and feedforward layers have been examined in the dynamically stable walking problem of two-legged robots. In detail, hybrid neural controllers, in which long short-term memory type of neuron models employed at recurrent layers, are utilized in the feedback and feedforward paths. To train these neural networks, supervised learning data sets are created by using a biped robot platform which is controlled by a central pattern generator. Then, the ability of the neural networks to perform stable gait by controlling the robot platform is examined under various ground conditions in the simulation environment. After that, the stable walking generation capacity of the neural networks and the central pattern generators are compared with each other. It is shown that the inclusion of recurrent layer provides smooth transition and control between stance and flight motion phases and L 2 regularization is beneficial for walking performance. Finally, the proposed hybrid neural network models are found to be more successful gait controllers than the central pattern generator, which is employed to generate data sets used in training.

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Section: Introductionmentioning

confidence: 99%

Two-Legged Robot Motion Control With Recurrent Neural Networks

Catalbas

Morgül

2022

J Intell Robot Syst

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“…In general, lots of experiences are needed to turn the parameters of the controller for humanoid walking robots [1,2]. At the same time, the turned parameters could be out of operation once external disturbances occurred [3,4]. It is still a giant challenge for humanoid robots to walk autonomously in disturbed environments.…”

Section: Introductionmentioning

confidence: 99%

Data-Based Control for Humanoid Robots Using Support Vector Regression, Fuzzy Logic, and Cubature Kalman Filter

Wang

Chen

et al. 2016

Mathematical Problems in Engineering

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Time-varying external disturbances cause instability of humanoid robots or even tip robots over. In this work, a trapezoidal fuzzy least squares support vector regression- (TF-LSSVR-) based control system is proposed to learn the external disturbances and increase the zero-moment-point (ZMP) stability margin of humanoid robots. First, the humanoid states and the corresponding control torques of the joints for training the controller are collected by implementing simulation experiments. Secondly, a TF-LSSVR with a time-related trapezoidal fuzzy membership function (TFMF) is proposed to train the controller using the simulated data. Thirdly, the parameters of the proposed TF-LSSVR are updated using a cubature Kalman filter (CKF). Simulation results are provided. The proposed method is shown to be effective in learning and adapting occasional external disturbances and ensuring the stability margin of the robot.

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“…Juang [14] developed a learning scheme using a fuzzy controller combined with a linearized inverse biped model to generate biped walking gaits. Henaff et al [15] described experimental results regarding the real-time implementation of neural network (NN) and the dynamic back propagation through time algorithm to learn the biped robot balance. Ferreira et al [16] compared two intelligent learning control approaches, SVR and NF network, for the realization of the real-time balance control of an eight-link biped.…”

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confidence: 99%

A UKF-Based Predictable SVR Learning Controller for Biped Walking

Wang

Liu

Chen

et al. 2013

IEEE Trans. Syst. Man Cybern, Syst.

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An unscented Kalman filter (UKF)-based predictable support vector regression (SVR) learning controller is proposed to improve the flexibility of biped walking robots. After estimating the biped states of the next moment using a UKF, an SVR learning controller with the predicted biped states is implemented to ensure the zero moment point (ZMP) stability. Using the predicted biped states, the SVR learning controller can predictably adjust the posture of the trunk timely and properly to adapt to the dynamic posture of the whole body. The flexibility of biped robots is enhanced by the proposed method, which is promising for realizing the stable biped walking in unstructured environments. Simulation and experimental results demonstrate the superiority of the proposed methods.

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Real time implementation of CTRNN and BPTT algorithm to learn on-line biped robot balance: Experiments on the standing posture

Cited by 14 publications

References 45 publications

Two-Legged Robot Motion Control With Recurrent Neural Networks

Two-Legged Robot Motion Control With Recurrent Neural Networks

Data-Based Control for Humanoid Robots Using Support Vector Regression, Fuzzy Logic, and Cubature Kalman Filter

A UKF-Based Predictable SVR Learning Controller for Biped Walking

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