Deep Learning of Proprioceptive Models for Robotic Force Estimation

Erik Berger, Daniel Eger Passos, Steve Grehl, Heni Ben Amor, Bernhard Jung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many robotic tasks require fast and accurate force sensing capabilities to ensure adaptive behavior execution. While dedicated force-torque (FT) sensors are a common option, such devices induce extra costs, need additional power supply, and add weight to otherwise light-weight robotic systems. This paper presents a machine learning approach for estimating external forces acting on a robot based on common internal sensors only. In the training phase, a behavior-specific proprioceptive model is learned as compact representation of the expected proprioceptive feedback during task execution. First, the proprioceptive sensors relevant for the given behavior are identified using information-theoretic measures. Then, the proprioceptive model is learned using deep learning techniques. During behavior execution, the proprioceptive model is applied to actual sensor readings for estimation of external forces. Experiments performed with the UR5 robot demonstrate the ability for fast and accurate force estimation even in situations where a dedicated commercial FT sensor is not applicable.

Original languageEnglish (US)
Title of host publication2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4258-4264
Number of pages7
ISBN (Electronic)9781728140049
DOIs
StatePublished - Nov 2019
Event2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 - Macau, China
Duration: Nov 3 2019Nov 8 2019

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
CountryChina
CityMacau
Period11/3/1911/8/19

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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