Bong, Jae Hwan Jung, Suhun Kim, Junhwi Park, Shinsuk 2024-01-19T10:32:22Z 2024-01-19T10:32:22Z 2023-01-13 2022-12 2313-7673 https://pubs.kist.re.kr/handle/201004/114210 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. English MDPI AG Standing Balance Control of a Bipedal Robot Based on Behavior Cloning Article 10.3390/biomimetics7040232 1 Biomimetics, v.7, no.4 Biomimetics 7 4 Y scie scopus 000902355300001 2-s2.0-85144923574 Engineering, Multidisciplinary Materials Science, Biomaterials Engineering Materials Science Article WALKING LEVEL biped robots robot motion intelligent robots robot learning behavior cloning