Donald C. Wunsch
Papers
17
Total Citations
269
H-Index
9
About
Donald C. Wunsch is a leading researcher in reinforcement learning, adaptive dynamic programming (ADP), and intelligent control for autonomous systems. His work bridges the gap between theoretical advances in machine learning and practical robotic applications, particularly in mobile robot navigation, path planning, and human-robot interaction. Wunsch pioneered the development of supervised actor-critic reinforcement learning (69 citations), which improves policy learning efficiency, and introduced data-driven off-policy reinforcement learning for human-robot interaction without velocity measurement (51 citations). He has also made significant contributions to neuro-fuzzy control, heuristic dynamic programming, and evolutionary algorithms for area coverage and search tasks. His research on embedded real-time neuro-fuzzy controllers (27 citations) and RAM-based neural networks for collision avoidance (12 citations) demonstrates a commitment to computationally efficient, deployable solutions. Notably, Wunsch developed the LabRat™ miniature robot platform (9 citations), an accessible tool for students, researchers, and hobbyists to experiment with autonomous robotics. With over 200 citations across his most influential papers, Wunsch’s work continues to shape the fields of adaptive control, reinforcement learning, and intelligent robotics.
Research Focus
Key Achievements
Top Papers
- 1Supervised Actor-Critic Reinforcement Learning69 citations · 2012
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- 3An Embedded Real-Time Neuro-Fuzzy Controller for Mobile Robot Navigation27 citations · 2005
- 4Adaptive task allocation for search area coverage23 citations · 2009
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- 6A RAM-based neural network for collision avoidance in a mobile robot12 citations · 2004
- 7Divide and conquer evolutionary TSP solution for vehicle path planning12 citations · 2008
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