Information-efficient model identification for tensegrity robot locomotion

Shaojun Zhu, David Surovik, Kostas Bekris, Abdeslam Boularias

Research output: Contribution to conferencePaperpeer-review


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.

Original languageEnglish (US)
Number of pages7
StatePublished - 2018
Event2018 AAAI Spring Symposium - Palo Alto, United States
Duration: Mar 26 2018Mar 28 2018


Conference2018 AAAI Spring Symposium
Country/TerritoryUnited States
CityPalo Alto

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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