Biped Robot Data-driven Gait Trajectory Genesis for Traipse Ground Conditions

This paper presents a data-driven gait model for continuous parameterization of joint kinematics which yields the genesis of biped robot trajectory. This work employed data-driven approaches such as Deep Neural Network (DNN) and Long Short Term Memory (LSTM) for parameterization using the human loco...

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Bibliographic Details
Published in2022 IEEE Delhi Section Conference (DELCON) pp. 1 - 6
Main Authors Patel, Suchit, Singh, Bharat, Kumar, Rajesh
Format Conference Proceeding
LanguageEnglish
Published IEEE 11.02.2022
Subjects
biped robot
Data-driven model
Deep learning
Gait model
Kinematics
Legged locomotion
Neural networks
Performance evaluation
Predictive models
Trajectory
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Summary:This paper presents a data-driven gait model for continuous parameterization of joint kinematics which yields the genesis of biped robot trajectory. This work employed data-driven approaches such as Deep Neural Network (DNN) and Long Short Term Memory (LSTM) for parameterization using the human locomotion data-set which consists of 10-able subjects walking data on varying inclines and speeds. It allows a smooth and non-switching prediction surface which provides the reference gait trajectory. Additionally, to constrain the model from following the high variance points from the mean trajectory, a loss function that incorporates the standard error of the inter-subject mean is also proposed. Performance evaluation shows that the LSTM performs far better than the DNN in terms of mean and max error for both trained and untrained data-set. Finally, the impact of varying speeds with an incline on the predicted kinematic trajectory for both models is also presented.
DOI:10.1109/DELCON54057.2022.9753290