Name
Title | ||
|---|---|---|
A Novel Dynamic Movement Primitives-based Skill Learning and Transfer Framework for Multi-Tool Use |
Abstract | ||
|---|---|---|
Dynamic Movement Primitives (DMPs) is a general method for learning skills from demonstrations. Most previous research on DMP has focused on point to point skill learning and training, and the skills learned are usually generalized based on the same tool or manipulator. There is rare research on skill learning and transfer between two or more different tools. For this problem, a new DMP-based skill learning and transfer framework is proposed for the use of multiple tools. It consists of two types of skills: Object Effective (OE) skills and State Switching (SS) skills. OE skills consider the tools' limited forcing areas that can be expressed as constrained inequalities, and extract skills from demonstrations. It can then be generalized along with changes in the shape and range of influence of a new tool. SS skill is used to connect OE skills and implement changes of contact points of the object and tool. Finally, the two skills are integrated and used to realize the transfer of skills from the demonstrated tool to the new tool. An experiment is conducted to verify the effectiveness of the proposed framework, and the procedural solutions and the final manipulation effect are shown in detail. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ICCA54724.2022.9831826 | 2022 IEEE 17th International Conference on Control & Automation (ICCA) |
Keywords | DocType | ISSN |
dynamic movement primitives,DMP,transfer framework,SS skill,multitool use,object effective skill,skill learning,OE skill,state switching skill,manipulator,skill transfer | Conference | 1948-3449 |
ISBN | Citations | PageRank |
978-1-6654-9573-8 | 0 | 0.34 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenyu Lu | 1 | 0 | 1.01 |
| Ning Wang | 2 | 230 | 87.46 |
| Miao Li | 3 | 0 | 0.34 |
| Chenguang Yang | 4 | 2213 | 138.71 |