Data Driven Inverse Kinematics of Soft Robots using Local Models
Authors
Fredrik Holsten, Morten Pol Engell-Nørregård, Sune Darkner, and Kenny Erleben
Abstract
Soft robots are advantageous in terms of flexibility, safety, and adaptability. It is challenging to find efficient computational approaches for planning and controlling their motion. This work takes a direct data-driven approach to learn the kinematics of the three-dimensional shape of a soft robot, by using visual markers. No prior information about the robot at hand is required. The model is oblivious to the design of the robot and the type of actuation system. This allows adaptation to erroneous manufacturing. We present a highly versatile and inexpensive learning cube environment for collecting and analyzing data. We prove that using multiple, lower-order models of data opposed to one global, higher-order model, will reduce the required data quantity, time complexity, and memory complexity significantly without compromising accuracy. Further, our approach allows for embarrassing parallelism. Yielding an overall much more simple and efficient approach.
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