Name
Abstract | ||
|---|---|---|
Task-space control of redundant robot systems based on analytical models is known to be susceptive to modeling errors. Data-driven model learning methods may present an interesting alternative approach. However, learning models for task-space tracking control from sampled data is an ill-posed problem. In particular, the same input data point can yield many different output values, which can form a nonconvex solution space. Because the problem is ill-posed, models cannot be learned from such data using common regression methods. While learning of task-space control mappings is globally ill-posed, it has been shown in recent work that it is locally a well-defined problem. In this paper, we use this insight to formulate a local kernel-based learning approach for online model learning for task-space tracking control. We propose a parametrization for the local model, which makes an application in task-space tracking control of redundant robots possible. The model parametrization further allows us to apply the kernel-trick and, therefore, enables a formulation within the kernel learning framework. In our evaluations, we show the ability of the method for online model learning for task-space tracking control of redundant robots. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TNNLS.2012.2201261 | IEEE Trans. Neural Netw. Learning Syst. |
Keywords | Field | DocType |
regression methods,task space control mappings,sampled data,robots,data driven model learning methods,kernel methods,real-time learning,learning (artificial intelligence),regression analysis,online kernel based learning,convex programming,online learning,nonconvex solution space,input data point,task space tracking robot control,output values,redundant robot systems,robot control,data handling,task-space tracking,learning artificial intelligence | Robot learning,Robot control,Online machine learning,Semi-supervised learning,Instance-based learning,Active learning (machine learning),Computer science,Unsupervised learning,Artificial intelligence,Iterative learning control,Machine learning | Journal |
Volume | Issue | ISSN |
23 | 9 | 2162-237X |
Citations | PageRank | References |
11 | 0.59 | 20 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| duy nguyentuong | 1 | 438 | 26.22 |
| Jan Peters | 2 | 3553 | 264.28 |