Information-Efficient Model Identification for Tensegrity Robot Locomotion
|Title||Information-Efficient Model Identification for Tensegrity Robot Locomotion|
|Year of Publication||2018|
|Authors||Zhu, S, Surovik, D, Bekris, KE, Boularias, A|
|Series Title||AAAI Spring Symposium Series 2018|
|City||Stanford University, CA, USA|
|Type||Symposium on Integrating Representation, Reasoning, Learning, and Execution for Goal Directed Autonomy|
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 data-efficient adaptation of a black-box 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 system identification challenge into an appropriate lower dimensional space. 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.