Name
Abstract | ||
|---|---|---|
For many applications such as compliant, accurate robot tracking control, dynamics models learned from data can help to achieve both compliant control performance as well as high tracking quality. Online learning of these dynamics models allows the robot controller to adapt itself to changes in the dynamics (e.g., due to time-variant nonlinearities or unforeseen loads). However, online learning in real-time applications - as required in control - cannot be realized by straightforward usage of off-the-shelf machine learning methods such as Gaussian process regression or support vector regression. In this paper, we propose a framework for online, incremental sparsification with a fixed budget designed for fast real-time model learning. The proposed approach employs a sparsification method based on an independence measure. In combination with an incremental learning approach such as incremental Gaussian process regression, we obtain a model approximation method which is applicable in real-time online learning. It exhibits competitive learning accuracy when compared with standard regression techniques. Implementation on a real Barrett WAM robot demonstrates the applicability of the approach in real-time online model learning for real world systems. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.neucom.2010.06.033 | Neurocomputing |
Keywords | Field | DocType |
competitive learning accuracy,robot control,real-time online model,inverse dynamics,real-time online model learning,real-time online learning,incremental online sparsification,standard regression technique,dynamics model,incremental learning approach,gaussian process regression,incremental gaussian process regression,real-time robot control,fast real-time model learning,sparse data,machine learning,real-time application,support vector regression,competitive learning,real time | Online machine learning,Competitive learning,Robot control,Semi-supervised learning,Active learning (machine learning),Computer science,Support vector machine,Artificial intelligence,Robot,Machine learning,Online model | Journal |
Volume | Issue | ISSN |
74 | 11 | Neurocomputing |
Citations | PageRank | References |
17 | 0.80 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| duy nguyentuong | 1 | 438 | 26.22 |
| Jan Peters | 2 | 3553 | 264.28 |