Temporal Logic Guided Motion Primitives for Complex Manipulation Tasks with User Preferences
Hao Wang, Haoyuan He, Weiwei Shang, Zhen Kan
Abstract
Dynamic movement primitives (DMPs) are a flexible trajectory learning scheme widely used in motion generation of robotic systems. However, existing DMP-based methods mainly focus on simple go-to-goal tasks. Motivated to handle tasks beyond point-to-point motion planning, this work presents temporal logic guided optimization of motion primitives, namely \mathbf{PI}^{\mathbf{BB}-\mathbf{TL}}\mathbf{PI}^{\mathbf{BB}-\mathbf{TL}} algorithm, for complex manipulation tasks with user preferences. In particular, weighted truncated linear temporal logic (wTLTL) is incorporated in the \mathbf{PI}^{\mathbf{BB}-\mathbf{TL}}\mathbf{PI}^{\mathbf{BB}-\mathbf{TL}} algorithm, which not only enables the encoding of complex tasks that involve a sequence of logically organized action plans with user preferences, but also provides a convenient and efficient means to design the cost function. The black-box optimization is then adapted to identify optimal shape parameters of DMPs to enable motion planning of robotic systems. The effectiveness of the \mathbf{PI}^{\mathbf{BB}-\mathbf{TL}}\mathbf{PI}^{\mathbf{BB}-\mathbf{TL}} algorithm is demonstrated via simulation and experiment.
BibTeX
@inproceedings{icra2022_temporallogicgui,
title = {Temporal Logic Guided Motion Primitives for Complex Manipulation Tasks with User Preferences},
author = {Hao Wang and Haoyuan He and Weiwei Shang and Zhen Kan},
booktitle = {ICRA 2022},
year = {2022}
}