Neural-Network-Controlled Spring Mass Template for Humanoid Running

Xin, Songyan; Delhaisse, Brian; You, Yangwei; Zhou, Chengxu; Shahbazi, Mohammad; Tsagarakis, Nikos G.

doi:10.1109/iros.2018.8593403

Cited by 8 publications

(6 citation statements)

References 22 publications

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“…Xin et al. [43] applied a data‐driven method for humanoid running, which uses the neural network (NN)‐based controller and the whole‐body controller to guide the actual robot control. Mistry et al.…”

Section: Model‐based Gait Control Methodsmentioning

confidence: 99%

An overview on bipedal gait control methods

Hu,

Xie,

Gao

et al. 2023

IET Collab Intel Manufact

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Bipedal gait control has always been a very challenging issue due to the multi‐joint and non‐linear structure of humanoid robots and frequent robot–environment interactions. To realise stable and robust bipedal walking, many aspects including robot modelling, gait stability and environmental adaptivity should be considered to design the gait control method. In this paper, a general description of bipedal gait and the corresponding evaluation indicators are introduced. Moreover, the existing bipedal gait control methods are classified into model‐based gait, stability criterion‐based gait and learning strategy‐based gait and a comprehensive review is conducted. Finally, the existing challenges and development trends of bipedal gait control are presented.

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Section: Model‐based Gait Control Methodsmentioning

confidence: 99%

An overview on bipedal gait control methods

Hu,

Xie,

Gao

et al. 2023

IET Collab Intel Manufact

View full text Add to dashboard Cite

show abstract

“…The corresponding flat outputs can then be computed via (12) and (13). Original states and inputs are in turn determined by (7) and (8). Apart from being important on its own, solution to the stance phase problem for all possible stance states constructs a value function for the stance dynamics.…”

Section: A Stance Phase Optimal Control -Flatness-based Solutionmentioning

confidence: 99%

“…The corresponding flat outputs can then be computed via ( 12) and (13). Original states and inputs are in turn determined by (7) and (8).…”

Section: A Stance Phase Optimal Control -Flatness-based Solutionmentioning

confidence: 99%

“…From the physical embodiment of SLIP principles in Raibert's early hoppers [4] to compliant legged bipeds designed by Hurst and colleagues [5], [6], this promise of energetic economy through passive compliance has subsequently motivated many successful robot designs. Beyond design, the SLIP model has also served as a common template model [7] to guide the control of hopping and running, both for robots that incorporate physical compliant mechanisms [8], [9] and those that provide active compliance [10] (e.g., via transparent actuation [11]).…”

Section: Introductionmentioning

confidence: 99%

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Optimal Control of a Differentially Flat Two-Dimensional Spring-Loaded Inverted Pendulum Model

Chen

Wensing

Zhang

2020

IEEE Robot. Autom. Lett.

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This paper considers the optimal control problem of an extended spring-loaded inverted pendulum (SLIP) model with two additional actuators for active leg length and hip torque modulation. These additional features arise naturally in practice, allowing for consideration of swing leg kinematics during flight and active control over stance dynamics. On the other hand, nonlinearity and the hybrid nature of the overall SLIP dynamics introduce challenges in the analysis and control of the model. In this paper, we first show that the stance dynamics of the considered SLIP model are differentially flat, which has a strong implication regarding controllability of the stance dynamics. Leveraging this powerful property, a tractable optimal control strategy is developed. This strategy enables online solution while also treating the hybrid nature of the SLIP dynamics. Together with the optimal control strategy, the extended SLIP model grants active disturbance rejection capability at any point during the gait. Performance of the proposed control strategy is demonstrated via numerical tests and shows significant advantage over existing methods.

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“…Recently, researchers have introduced artificial neural networks in legged robots to explore the possibility of realtime implementation using non-linear models. Xin et al [16] adopted a neural network to generate referential foot placements for a bipedal robot hopping and running based on a spring-loaded inverted pendulum model. The neural network eliminates the need for solving non-linear equations on-line.…”

Section: Introductionmentioning

confidence: 99%

Normalized Neural Network for Energy Efficient Bipedal Walking Using Nonlinear Inverted Pendulum Model

Wang

Hudson

Yao

et al. 2019

2019 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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In this paper, we present a novel approach for bipedal walking pattern generation. The proposed method is designed based on 2D inverted pendulum model. All control variables are optimized for an energy efficient gait. To obviate the need of solving non-linear dynamics on-line, a deep neural network is adopted for fast non-linear mapping from desired states to control variables. Normalized dimensionless data is generated to train the neural network, therefore, the trained neural network can be applied to bipedal robots of any size, without any specific modification. The proposed method is later verified through numerical simulations. Simulation results demonstrated that the proposed approach can generate feasible walking motions, and regulate robot's walking velocity successfully. Its disturbance rejection capability was also validated.

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Neural-Network-Controlled Spring Mass Template for Humanoid Running

Cited by 8 publications

References 22 publications

An overview on bipedal gait control methods

An overview on bipedal gait control methods

Optimal Control of a Differentially Flat Two-Dimensional Spring-Loaded Inverted Pendulum Model

Normalized Neural Network for Energy Efficient Bipedal Walking Using Nonlinear Inverted Pendulum Model

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