Authors:Thomas George Thuruthel¹;Egidio Falotico¹;Matteo Cianchetti¹;Federico Renda² andCecilia Laschi¹

Affiliations:¹The Biorobotics Institute, Italy;²Khalifa University, United Arab Emirates

Keyword(s):Soft Robots, Machine Learning, Inverse Statics, Inverse Dynamics, Steady State Model, Neural Networks.

RelatedOntology Subjects/Areas/Topics:Informatics in Control, Automation and Robotics ;Intelligent Control Systems and Optimization ;Machine Learning in Control Applications ;Modeling, Simulation and Architectures ;Neural Networks Based Control Systems ;Robot Design, Development and Control ;Robotics and Automation

Abstract:This paper presents a learning model for obtaining global inverse statics solutions for redundant soft robots. Our motivation begins with the opinion that the inverse statics problem is analogous to the inverse kinematics problem in the case of soft continuum manipulators. A unique inverse statics formulation and data sampling method enables the learning system to circumvent the main roadblocks of the inverting problem. Distinct from previous researches, we have addressed static control of both position and orientation of soft robots. Preliminary tests were conducted on the simulated model of a soft manipulator. The results indicate that learning based approaches could be an effective method for modelling and control of complex soft robots, especially for high dimensional redundant robots.