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About

Thomas Geijtenbeek is a leading researcher in biomechanics and physics-based animation, whose work bridges the gap between biological movement and computational simulation. His primary research areas include reinforcement learning for locomotion, neuromuscular control, and physics-based character animation. Geijtenbeek’s most impactful contribution is his 2023 paper on natural and robust walking using reinforcement learning without demonstrations in high-dimensional musculoskeletal models, which has garnered 10 citations. This work demonstrates how artificial agents can achieve human-like bipedal walking that is resilient to uncertain ground conditions, offering profound insights into the neural control of movement. He has also made notable strides in animating virtual characters through physics-based simulation, a field he has helped advance since the early 2010s. His most recent 2025 study on proprioceptive reflexes reveals how simple neural networks can generate a variety of bipedal gaits, challenging previous assumptions about the complexity of reflex-based control. Geijtenbeek’s research not only enhances our understanding of human locomotion but also has practical applications in robotics, prosthetics, and computer animation, making him a pivotal figure in computational biomechanics.