Document Type
Article
Source Publication Title
PETRA 2021
Abstract
In this paper, we present a novel method to learn end-to-end visuomotor policies for robotic manipulators. The method computes state-action mappings in a supervised learning manner from video demonstrations and robot trajectories. We show that the robot learns to perform different tasks by associating image features with the corresponding movement primitives of different grasp poses. To evaluate the effectiveness of the proposed learning method, we conduct experiments with a PR2 robot in a simulation environment. The purpose of these experiments is to evaluate the system’s ability to perform manipulation tasks.
Publication Date
7-2-2021
Language
English
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Theofanidis, Michail; Bozcuoglu, Asil Kaan; Neumann, Michael; Makedon, Fillia; Kyrarini, Maria; Beetz, Michael; and Cloud, Jeo, "Learning Visuomotor Policies with Deep Movement Primitives" (2021). Association of Computing Machinery Open Access Agreement Publications. 8.
https://mavmatrix.uta.edu/utalibraries_acmoapubs/8