Online Shape Estimation based on Tactile Sensing and Deformation Modeling for Robot Manipulation
Jose Sanchez, Carlos M. Mateo, Juan Antonio Corrales, Belhassen-Chedli Bouzgarrou, Youcef Mezouar
Abstract
Precise robot manipulation of deformable objects requires an accurate and fast estimation of their shape as they deform. So far, visual sensing has been mostly used to solve this issue, but vision sensors are sensitive to occlusions, which might be inevitable when manipulating an object with robot. To address this issue, we present a modular pipeline to track the shape of a soft object in an online manner by coupling tactile sensing with a deformation model. Using a model of a tactile sensor, we compute the magnitude and location of a contact force and apply it as an external force to the deformation model. The deformation model then updates the nodal positions of a mesh that describes the shape of the deformable object. The proposed sensor model and pipeline, are evaluated using a Shadow Dexterous Hand equipped with BioTac sensors on its fingertips and an RGB-D sensor.
BibTeX
@inproceedings{iros2018_onlineshapeestim,
title = {Online Shape Estimation based on Tactile Sensing and Deformation Modeling for Robot Manipulation},
author = {Jose Sanchez and Carlos M. Mateo and Juan Antonio Corrales and Belhassen-Chedli Bouzgarrou and Youcef Mezouar},
booktitle = {IROS 2018},
year = {2018}
}