Uncertainty-Aware Manipulation Planning Using Gravity and Environment Geometry
Felix von Drigalski, Kazumi Kasaura, Cristian C. Beltran-Hernandez, Masashi Hamaya, Kazutoshi Tanaka, Takamitsu Matsubara
Abstract
Factory automation robot systems often depend on specially-made jigs that precisely position each part, which increases the system's cost and limits flexibility. We propose a method to determine the 3D pose of an object with high precision and confidence, using only parallel robotic grippers and no parts-specific jigs. Our method automatically generates a sequence of actions that ensures that the real-world position of the physical object matches the system's assumed pose to sub-mm precision. Furthermore, we propose the use of “extrinsic” actions, which use gravity, the environment and the gripper geometry to significantly reduce or even eliminate the uncertainty about an object's pose. We show in simulated and real-robot experiments that our method outperforms our previous work, at success rates over 95%.
BibTeX
@inproceedings{ral2022_uncertaintyaware,
title = {Uncertainty-Aware Manipulation Planning Using Gravity and Environment Geometry},
author = {Felix von Drigalski and Kazumi Kasaura and Cristian C. Beltran-Hernandez and Masashi Hamaya and Kazutoshi Tanaka and Takamitsu Matsubara},
booktitle = {RA-L 2022},
year = {2022}
}