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About

I. Grondman is a leading researcher in reinforcement learning (RL), with a particular focus on actor-critic algorithms and their application to real-time robotic control. His most influential contribution is the seminal survey, “A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients” (2012), which has garnered over 1,040 citations. This work systematically analyzed policy-gradient-based actor-critic methods, highlighting their ability to search for optimal policies with low-variance gradient estimates—a key advantage that has driven their widespread adoption in fields like robotics. Grondman’s applied research centers on time-optimal control for robotic systems, notably demonstrated in his work on a badminton robot. Through studies such as “Comparison of model-free and model-based methods for time optimal hit control of a badminton robot” (2014) and “Model-free and model-based time-optimal control of a badminton robot” (2013), he explored how RL can achieve precise, high-speed motion control, including tasks like serving a shuttlecock from rest to a target state with non-zero velocity. His work on learning-rate-free RL for real-time motion control further underscores his commitment to practical, deployable solutions. Grondman’s research bridges foundational RL theory and tangible robotic applications, making him a key figure in advancing autonomous, adaptive control systems.