Uncertainty-aware Motion Planning based on Stochastic Forward/Inverse Kinematics Models for Tensegrity Manipulators
Yuhei Yoshimitsu, Takayuki Osa, Heni Ben Amor, Shuhei Ikemoto
Abstract
Robots whose shape and stiffness are determined by internal forces generally have complex shape-stiffness relationships that depend on their structure. As a result, there are difficulties such as a decrease in shape reproducibility when the robot is not stiff, and a decrease in the range of motion when the robot is stiff. In this study, we propose a motion planning method that balances shape and stiffness by learning forward and inverse kinematics using a stochastic neural network (NN) and using the uncertainty that can be evaluated by the NN. Through experiments using a tensegrity manipulator with 40 actuators and 20 degrees of freedom in bending posture, we verify the validity of the proposed method.
BibTeX
@inproceedings{iros2025_uncertaintyaware,
title = {Uncertainty-aware Motion Planning based on Stochastic Forward/Inverse Kinematics Models for Tensegrity Manipulators},
author = {Yuhei Yoshimitsu and Takayuki Osa and Heni Ben Amor and Shuhei Ikemoto},
booktitle = {IROS 2025},
year = {2025}
}