Deep Functional Predictive Control (deep-FPC): Robot Pushing 3-D Cluster Using Tactile Prediction
Kiyanoush Nazari, Gabriele Gandolfi, Zeynab Talebpour, Vishnu Rajendran, Willow Mandil, Paolo Rocco, Amir Ghalamzan-E.
Abstract
This paper introduces a novel approach to address the problem of Physical Robot Interaction (PRI) during robot pushing tasks. The approach uses a data-driven forward model based on tactile predictions to inform the controller about potential future movements of the object being pushed, such as a strawberry stem, using a robot tactile finger. The model is integrated into a Deep Functional Predictive Control (d-FPC) system to control the displacement of the stem on the tactile finger during pushes. Pushing an object with a robot finger along a desired trajectory in 3D is a highly nonlinear and complex physical robot interaction, especially when the object is not stably grasped. The proposed approach controls the stem movements on the tactile finger in a prediction horizon. The effectiveness of the proposed FPC is demonstrated in a series of tests involving a real robot pushing a strawberry in a cluster. The results indicate that the d-FPC controller can successfully control PRI in robotic manipulation tasks beyond the handling of strawberries. The proposed approach offers a promising direction for addressing the challenging PRI problem in robotic manipulation tasks.
BibTeX
@inproceedings{iros2023_deepfunctionalpr,
title = {Deep Functional Predictive Control (deep-FPC): Robot Pushing 3-D Cluster Using Tactile Prediction},
author = {Kiyanoush Nazari and Gabriele Gandolfi and Zeynab Talebpour and Vishnu Rajendran and Willow Mandil and Paolo Rocco and Amir Ghalamzan-E.},
booktitle = {IROS 2023},
year = {2023}
}