Learning global inverse statics solution for a redundant soft robot

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

doi:10.5220/0005979403030310

Learning global inverse statics solution for a redundant soft robot

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

Mechanical & Nuclear Engineering

Scuola Superiore Sant'Anna

Research output: Contribution to conference › Paper › peer-review

34 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 language	British English
Pages	303-310
Number of pages	8
DOIs	https://doi.org/10.5220/0005979403030310
State	Published - 2016
Event	13th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2016 - Lisbon, Portugal Duration: 29 Jul 2016 → 31 Jul 2016

Conference

Conference	13th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2016
Country/Territory	Portugal
City	Lisbon
Period	29/07/16 → 31/07/16

Keywords

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

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10.5220/0005979403030310

Cite this

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title = "Learning global inverse statics solution for a redundant soft robot",

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.",

keywords = "Inverse Dynamics, Inverse Statics, Machine Learning, Neural Networks., Soft Robots, Steady State Model",

author = "Thuruthel, {Thomas George} and Egidio Falotico and Matteo Cianchetti and Federico Renda and Cecilia Laschi",

note = "Funding Information: The authors would like to acknowledge the support by the European Commission through the I-SUPPORT project (HORIZON 2020 PHC-19, #643666).The authors would like to thank Italian Ministry of Foreign Affairs and International Cooperation DGSP-UST for the support through Joint Laboratory on Biorobotics Engineering project. Publisher Copyright: Copyright {\textcopyright} 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.; 13th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2016 ; Conference date: 29-07-2016 Through 31-07-2016",

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AU - Falotico, Egidio

AU - Cianchetti, Matteo

AU - Renda, Federico

AU - Laschi, Cecilia

N1 - Funding Information: The authors would like to acknowledge the support by the European Commission through the I-SUPPORT project (HORIZON 2020 PHC-19, #643666).The authors would like to thank Italian Ministry of Foreign Affairs and International Cooperation DGSP-UST for the support through Joint Laboratory on Biorobotics Engineering project. Publisher Copyright: Copyright © 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Inverse Dynamics

KW - Inverse Statics

KW - Machine Learning

KW - Neural Networks.

KW - Soft Robots

KW - Steady State Model

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T2 - 13th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2016

Y2 - 29 July 2016 through 31 July 2016

ER -

Learning global inverse statics solution for a redundant soft robot

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Keywords

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