Low-Cost Wireless Modular Soft Tensegrity Robots

Abstract

Completely soft robots are emerging as a compelling new platform for exploring and operating in unstructured, rugged, and dynamic environments. Unfortunately, the very properties which make soft robots so appealing also make them difficult to accurately model, scalably design, and robustly control. One of the outstanding obstacles to exploring these challenges is the relative lack of low-cost entry-level investigative model systems. In this paper we describe the design and implementation of a low-cost entry-level soft robotics platform based upon modular tensegrity structures. This modular platform can scale across a variety of shapes and sizes and is capable of untethered control. We then demonstrate how unsupervised learning algorithms can be used to produce vibration-based locomotion.

Zongliang (Jerry) Ji

PhD student @ U of Toronto

My research interests include machine learning for healthcare and computational biology.