Papers
19
Total Citations
327
H-Index
10
About
Tim Verbelen is a researcher at the intersection of autonomous robotics, machine learning, and computational neuroscience, with a particular focus on applying biologically inspired principles to artificial intelligence systems. His most significant contribution lies in advancing the **active inference** framework — a theoretical model rooted in how biological organisms perceive and act — as a practical tool for enabling autonomous robot behavior. His 2021 paper on hierarchical active inference for robot navigation has garnered 79 citations, establishing him as a leading voice in translating this neuroscientific framework into real-world robotics applications. Verbelen's work spans robot perception, manipulation, and spatial cognition. He has developed generative state space models for active inference agents, explored learning-from-demonstration approaches for robotic grasping, and pioneered biologically inspired SLAM architectures that draw parallels to the brain's hippocampal and entorhinal systems. His earlier contributions to deep reinforcement learning for sensor fusion and distributed cyber-physical systems demonstrate a broad technical foundation. More recently, he has extended his work into advanced radar imaging for mobile robots. With over 280 cumulative citations across his top papers, Verbelen's research bridges neuroscience, probabilistic modeling, and practical robotics in a distinctly interdisciplinary way.
Research Focus
Key Achievements
Top Papers
- 1Robot navigation as hierarchical active inference79 citations · 2021
- 2Learning Generative State Space Models for Active Inference39 citations · 2020
- 3Learning robots to grasp by demonstration33 citations · 2020
- 4Sensor fusion for robot control through deep reinforcement learning26 citations · 2017
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- 6Active Vision for Robot Manipulators Using the Free Energy Principle23 citations · 2021
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- 10Learning to Grasp from a Single Demonstration10 citations · 2018