Learning global inverse statics solution for a redundant soft robot

Thomas George Thuruthel, Egidio Falotico, Matteo Cianchetti, Federico Renda, Cecilia Laschi

Research output: Contribution to conferencePaperpeer-review

30 Scopus citations

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.

Original languageBritish English
Pages303-310
Number of pages8
DOIs
StatePublished - 2016
Event13th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2016 - Lisbon, Portugal
Duration: 29 Jul 201631 Jul 2016

Conference

Conference13th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2016
Country/TerritoryPortugal
CityLisbon
Period29/07/1631/07/16

Keywords

  • Inverse Dynamics
  • Inverse Statics
  • Machine Learning
  • Neural Networks.
  • Soft Robots
  • Steady State Model

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