Estimating Tactile Models of Heterogeneous Deformable Objects in Real Time

Shaoxiong Yao, Kris Hauser

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

Abstract

This paper introduces a method for learning the force response of heterogeneous, deformable objects directly from robot sensor data without prior knowledge. The method estimates an object's force response given robot force or torque measurements using a novel volumetric stiffness field representation and point-based contact simulator. The stiffness of each point colliding with the robot is estimated independently and is updated upon each observed measurement using a projected diagonal Kalman filter. Experiments show that this method can update a stiffness field over 105 points at 23 Hz or higher, and is more accurate than learning-based methods in predicting torque response while touching artificial plants. The method can also be augmented with visual information to help extrapolate stiffness fields to distant parts of the touched object using only a small number of touches.

Original languageEnglish (US)
Title of host publicationProceedings - ICRA 2023
Subtitle of host publicationIEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages12583-12589
Number of pages7
ISBN (Electronic)9798350323658
DOIs
StatePublished - 2023
Event2023 IEEE International Conference on Robotics and Automation, ICRA 2023 - London, United Kingdom
Duration: May 29 2023Jun 2 2023

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2023-May
ISSN (Print)1050-4729

Conference

Conference2023 IEEE International Conference on Robotics and Automation, ICRA 2023
Country/TerritoryUnited Kingdom
CityLondon
Period5/29/236/2/23

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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