Variational Inference Data-driven Gait Model for Biped Trajectory Generation

Bharat Singh, Suchit Patel, Ankit Vijayvargiya, Rajesh Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2022 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages197-202
Number of pages6
ISBN (Electronic)9781665443579
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event1st IEEE IAS Global Conference on Emerging Technologies, GlobConET 2022 - Virtual, Arad, Romania
Duration: 20 May 202222 May 2022

Publication series

Name2022 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2022

Conference

Conference1st IEEE IAS Global Conference on Emerging Technologies, GlobConET 2022
Country/TerritoryRomania
CityVirtual, Arad
Period20/05/2222/05/22

Keywords

  • Bayesian Framework
  • Gait Model
  • Traipse Conditions
  • Variational Framework

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Safety, Risk, Reliability and Quality
  • Media Technology
  • Instrumentation

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