Standing Balance Control of a Bipedal Robot Based on Behavior Cloning

Authors
Bong, Jae HwanJung, SuhunKim, JunhwiPark, Shinsuk
Issue Date
2022-12
Publisher
MDPI AG
Citation
Biomimetics, v.7, no.4
Abstract
Bipedal robots have gained increasing attention for their human-like mobility which allows them to work in various human-scale environments. However, their inherent instability makes it difficult to control their balance while they are physically interacting with the environment. This study proposes a novel balance controller for bipedal robots based on a behavior cloning model as one of the machine learning techniques. The behavior cloning model employs two deep neural networks (DNNs) trained on human-operated balancing data, so that the trained model can predict the desired wrench required to maintain the balance of the bipedal robot. Based on the prediction of the desired wrench, the joint torques for both legs are calculated using robot dynamics. The performance of the developed balance controller was validated with a bipedal lower-body robotic system through simulation and experimental tests by providing random perturbations in the frontal plane. The developed balance controller demonstrated superior performance with respect to resistance to balance loss compared to the conventional balance control method, while generating a smoother balancing movement for the robot.
Keywords
WALKING; LEVEL; biped robots; robot motion; intelligent robots; robot learning; behavior cloning
ISSN
2313-7673
URI
https://pubs.kist.re.kr/handle/201004/114210
DOI
10.3390/biomimetics7040232
Appears in Collections:
KIST Article > 2022
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