Name
Title | ||
|---|---|---|
Reproduction of Human Demonstrations with a Soft-Robotic Arm based on a Library of Learned Probabilistic Movement Primitives |
Abstract | ||
|---|---|---|
In this paper we introduce a novel technique that aims to control a two-module bio-inspired soft-robotic arm in order to qualitatively reproduce human demonstrations. The main idea behind the proposed methodology is based on the assumption that a complex trajectory can be derived from the composition and asynchronous activation of learned parameterizable simple movements constituting a knowledge base. The present work capitalises on recent research progress in Movement Primitive (MP) theory in order to initially build a library of Probabilistic MPs (ProMPs), and subsequently to compute on the fly their proper combination in the task space resulting in the requested trajectory. At the same time, a model learning method is assigned with the task to approximate the inverse kinematics, while a replanning procedure handles the sequential and/or parallel ProMPs' asynchronous activation. Taking advantage of the mapping at the primitive-level that the ProMP framework provides, the composition is transferred into the actuation space for execution. The proposed control architecture is experimentally evaluated on a real soft-robotic arm, where its capability to simplify the trajectory control task for robots of complex unmodeled dynamics is exhibited. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ICRA46639.2022.9811627 | IEEE International Conference on Robotics and Automation |
DocType | Volume | Issue |
Conference | 2022 | 1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paris Oikonomou | 1 | 0 | 1.35 |
| Athanasios Dometios | 2 | 7 | 2.81 |
| Mehdi Khamassi | 3 | 0 | 0.34 |
| Costas S. Tzafestas | 4 | 153 | 25.95 |