Martin Brown
Papers
12
Total Citations
171
H-Index
7
About
Martin Brown is a leading researcher in reinforcement learning, control theory, and humanoid robotics, whose work bridges the gap between machine learning and complex physical systems. His most influential contributions focus on using reinforcement learning algorithms to solve challenging control problems, particularly for bipedal locomotion and balance. Brown’s 2015 paper on “Reinforcement learning analysis for a minimum time balance problem” (33 citations) and his related work on chaotic dynamics in temporal difference algorithms (31 citations) have been foundational in understanding how learning systems can achieve stable, optimal control with minimal prior knowledge. He has also made significant theoretical contributions, including establishing the functional equivalence between fuzzy inference systems and spline-based networks (29 citations), a result that has implications for interpretable AI. Brown’s research extends to practical robotics, where he has modeled and simulated humanoid locomotion, developed control strategies for ZMP bipedal walking, and explored compliant robot control. His work on value function learning with piecewise linear control (27 citations) further demonstrates his impact on the convergence and stability of reinforcement learning methods. With over 150 total citations across his most-cited papers, Brown’s research continues to influence both the theoretical foundations and real-world applications of intelligent control systems.
Research Focus
Key Achievements
Top Papers
- 1Reinforcement learning analysis for a minimum time balance problem33 citations · 2015
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- 4An analysis of value function learning with piecewise linear control27 citations · 2015
- 5Modelling and Simulation of the Locomotion of Humanoid Robots12 citations · 2010
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- 9Intelligent control for autonomous guided vehicles6 citations · 1991
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