Force-based Learning of Variable Impedance Skills for Robotic Manipulation

Fares J. Abu-Dakka*, Leonel Rozo, Darwin G. Caldwell

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

Numerous robotics tasks involve complex physical interactions with the environment, where the role of variable impedance skills and the information of contact forces are crucial for successful performance. The dynamicity of our environments demands robots to adapt their manipulation skills to a large variety of situations, where learning capabilities are necessary. In this context, we propose a framework to teach a robot to perform manipulation tasks by integrating force sensing and variable impedance control. This framework endows robots with force-based variable stiffness skills that become relevant when vision information is unavailable or uninformative. Such skills are built on stiffness estimations that are computed from human demonstrations, which are then used along with sensed forces, to encode a probabilistic model of the robot skill. The resulting model is later used to retrieve time-varying stiffness profiles. We study two different stiffness representations based on (i) Cholesky decomposition, and (ii) Riemannian manifolds. For validation, we use a simulation of a 2D mass-spring-damper system subject to external forces, and a real experiment where a 7-DoF robot learns to perform a valve-turning task by varying its Cartesian stiffness.

Original languageEnglish
Title of host publication2018 IEEE-RAS 18TH INTERNATIONAL CONFERENCE ON HUMANOID ROBOTS (HUMANOIDS)
EditorsT Asfour
PublisherIEEE
Pages278-285
Number of pages8
ISBN (Electronic)978-1-5386-7283-9
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventIEEE-RAS International Conference on Humanoid Robots - Beijing, China
Duration: 6 Nov 20189 Nov 2018
Conference number: 18

Publication series

NameIEEE-RAS International Conference on Humanoid Robots
PublisherIEEE
ISSN (Print)2164-0572

Conference

ConferenceIEEE-RAS International Conference on Humanoid Robots
Abbreviated titleHumanoids
CountryChina
CityBeijing
Period06/11/201809/11/2018

Keywords

  • TASK

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