Vivien van Veldhuizen

About

Vivien van Veldhuizen is a researcher at the intersection of robotics, control systems, and reinforcement learning. Her primary research focuses on developing intelligent, adaptive control strategies for agricultural robotics, with a particular emphasis on automating complex tasks such as fruit harvesting. In her most cited work, "Autotuning PID control using Actor-Critic Deep Reinforcement Learning" (2022), van Veldhuizen pioneers a novel approach to PID controller tuning by leveraging the Advantage Actor-Critic (A2C) algorithm. This exploratory study demonstrates how reinforcement learning can autonomously predict optimal PID parameters for a simulated robot arm designed for apple harvesting, effectively replacing manual, time-consuming tuning processes. By bridging classical control theory with modern deep reinforcement learning, she offers a scalable solution for robots operating in dynamic, unstructured environments like orchards. Though early in her career, with this foundational paper already garnering 2 citations, van Veldhuizen’s work signals a promising shift toward more autonomous and efficient agricultural robotics. Her research not only advances the field of robot control but also holds practical implications for improving the precision and productivity of automated harvesting systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Autotuning PID control using Actor-Critic Deep Reinforcement Learning

   2 citations · 2022

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