@inproceedings{58dc9b164a5544dcb7659b20749fe0fe,
title = "Variational Inference Data-driven Gait Model for Biped Trajectory Generation",
abstract = "Biped robot trajectory generation is a very complex task for real-world uneven terrain. This work presents the gait model based on data-driven to address the issue of traipse ground conditions. The data-driven approach efficiently extracts valuable information regarding the joint relationship efficiently. However, the models can suffer from the model-bias issue. Therefore, the model bias is addressed by considering the uncertainty into the model itself under Bayesian framework. In addition, the new objective function for training of data-driven model based on the integration variational inference with standard error is proposed. It helps the training algorithm to precisely follow the lower variance data-point along the gait cycle. Lastly, the proposed model is analysed based on the two scenarios: (a) constant speed 1m/s with varying incline, and (b) constant incline 0 degrees with varying speed.",
keywords = "Bayesian Framework, Gait Model, Traipse Conditions, Variational Framework",
author = "Bharat Singh and Suchit Patel and Ankit Vijayvargiya and Rajesh Kumar",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 1st IEEE IAS Global Conference on Emerging Technologies, GlobConET 2022 ; Conference date: 20-05-2022 Through 22-05-2022",
year = "2022",
doi = "10.1109/GlobConET53749.2022.9872440",
language = "English",
series = "2022 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "197--202",
booktitle = "2022 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2022",
address = "United States",
}