Duy Nguyen-Tuong has been pursuing his Ph.D. since 2007 at the Max-Planck Institute for Biological Cybernetics in the department of Bernhard Schölkopf under the supervision of Jan Peters. Before doing so, he studied control and automation engineering at the University of Stuttgart and the National University of Singapore.

His main research interest is the application of machine learning techniques in control and robotics. One of his research focuses is developing regression methods for online model learning in real-time which can be used, for example, in robot model-based control. This research objective is based on the insight that accurate analytical models, e.g., rigid body models, can not be obtained for sufficiently complex robot systems due to many unknown nonlinearities. In such cases, machine learning presents an appealing alternative for approximating those models from measured data. Learning from data will include all robot's nonlinearities resulting in a more accurate model and, thus, a better control performance. However, the main drawback of machine learning techniques is the excessive computational complexity preventing a straightforward application in robotics. Furthermore, in order to take full advantage of a learning approach, online learning is an absolute necessity as it allows the model adaption due to changes in the robot dynamics, load or the actuators.

Another research focus is learning multivalued models, i.e., a mapping from one to many. A multivalued model can play an important role in many robotics applications, such as learning inverse kinematics or operational space robot control. Learning such multivalued models from data can not be obtained using common regression methods, as standard regression techniques tend to average over multiple output solutions and, thus, yielding a degenerated mapping. For such cases, new regression techniques have to be developed to resolve the ambiguity of multivalued models.

To promote machine learning techniques in robotics and control, he co-organized a NIPS 2009 workshop on Probabilistic Approaches for Robotics and Control. More details of his research activities can be found at Research > Model Learning.

Research Interests: Robot Control, Dynamics, Machine Learning, Kernel Methods.
Biographical Information: Please see his curriculum vitae.
Publications: For the complete list of his publication, see here. Key references are shown below.
Collaborators: Stefan Schaal (USC), Bernhard Schölkopf and Jan Peters

Key References

1. Nguyen-Tuong, D.; Seeger, M.; Peters, J. (2009). Local Gaussian Process Regression for Real Time Online Model Learning and Control, Advances in Neural Information Processing Systems 22 (NIPS 2008), Cambridge, MA: MIT Press. [PDF]
   
   

2. Nguyen-Tuong, D.; Peters, J.; Seeger, M.; Schoelkopf, B. (2008). Computed Torque Control with Nonparametric Regressions Techniques, American Control Conference. [PDF]
   
   

3. Nguyen-Tuong, D.; Seeger, M.; Peters, J. (2009). Model Learning with Local Gaussian Process Regression, Advanced Robotics, 23, 15, pp.2015-2034. [PDF]
   
   

4. Nguyen-Tuong, D.; Peters, J. (2010). Incremental Sparsification for Real-time Online Model Learning, Proceedings of Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010). [PDF]
   
   

5. Nguyen-Tuong, D.; Peters, J. (2010). Using Model Knowledge for Learning Inverse Dynamics, IEEE International Conference on Robotics and Automation. [PDF]
   
   

Contact Information

Mail: Duy Nguyen-Tuong, Spemannstr. 38, 72076 Tuebingen, Germany
Phone: +49-7071-601-585
Fax: +49-7071-601-552
Email: duy@tuebingen.mpg.de