Duy Nguyen-Tuong has been a Ph.D. student from 2007 to 2011 at the Max-Planck Institute for Biological Cybernetics in the Robot Learning Lab (RoLL) 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
Duy has graduated with a Ph.D. and has moved to the research and development center of Robert Bosch GmbH in Schwieberdingen where he continues to work on machine learning and robotics albeit in an industrial setting.
Address: Robert Bosch GmbH, Postfach 30 02 40, 70442 Stuttgart
duy@robot-learning.de