Efficient Model Identification for Tensegrity Locomotion

TitleEfficient Model Identification for Tensegrity Locomotion
Publication TypeConference Paper
Year of Publication2018
AuthorsZhu, S, Surovik, D, Bekris, KE, Boularias, A
Conference NameIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Date Published10/2018
Conference LocationMadrid, Spain
Abstract

This paper aims to identify in a practical manner unknown physical parameters, such as mechanical models of actuated robot links, which are critical in dynamical robotic tasks. Key features include the use of an off-the-shelf physics engine and the Bayesian optimization framework. The task being considered is locomotion with a high-dimensional, compliant Tensegrity robot. A key insight, in this case, is the need to project the space of models into an appropriate lower dimensional space for time efficiency. Comparisons with alternatives indicate that the proposed method can identify the parameters more accurately within the given time budget, which also results in more precise locomotion control.

URLhttps://www.cs.rutgers.edu/~kb572/pubs/model_identification_tensegrity.pdf