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Robert Babuska
Researcher at Delft University of Technology
Publications - 381
Citations - 17611
Robert Babuska is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Fuzzy logic & Reinforcement learning. The author has an hindex of 56, co-authored 371 publications receiving 15388 citations. Previous affiliations of Robert Babuska include Carnegie Mellon University & Czech Technical University in Prague.
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Journal ArticleDOI
Optimal Control via Reinforcement Learning with Symbolic Policy Approximation
TL;DR: A novel method to construct a smooth policy represented by an analytic equation, obtained by means of symbolic regression is proposed and shows that the analytic control law performs at least equally well as the original numerically approximated policy, while it leads to much smoother control signals.
Proceedings ArticleDOI
Using prior knowledge to accelerate online least-squares policy iteration
TL;DR: This paper considers prior knowledge about the monotonicity of the control policy with respect to the system states, and introduces an approach that exploits this type of prior knowledge to accelerate a state-of-the-art RL algorithm called online least-squares policy iteration (LSPI).
Journal ArticleDOI
Modeling and identification of a strip guidance process with internal feedback
TL;DR: This new technique is proposed to reduce the closed-loop identification problem into an open-loop one by using a regulator to compensate the internal feedback due to the endless strip.
Proceedings ArticleDOI
Effective transfer learning of affordances for household robots
TL;DR: This paper proposes transfer learning of affordances to reduce the number of exploratory actions needed to learn how to use a new object and demonstrates through real-world experiments with the humanoid robot NAO that this method is able to speed up the use of a new type of garbage can by transferring the affordances learned previously for similar garbage cans.
Proceedings ArticleDOI
Online self-organizing adaptive fuzzy controller: Application to a nonlinear servo system
TL;DR: This paper presents a self-organizing adaptive fuzzy controller that works online that uses the data obtained online during the normal operation of the system to modify the structure of the fuzzy controller.