Martin Breidt

Max Planck Society, Sapienza University of Rome, Max Planck Institute for Biological Cybernetics

Papers

Total Citations

103

H-Index

About

Martin Breidt’s research sits at the intersection of robotics, human perception, and machine learning, with a focus on how machines can better understand and interact with the real world. He has made significant contributions to representation learning, particularly through his work on disentanglement—the ability to learn compact, semantically meaningful data representations. His 2019 paper on transferring inductive bias from simulation to reality introduced a new disentanglement dataset, addressing a critical bottleneck in AI: the costly gap between synthetic training data and real-world application. In robotics, Breidt co-developed a novel framework for closed-loop motion simulation using anthropomorphic manipulators, published in two parts (2010). This work, with over 48 combined citations, replaced traditional Stewart platforms with more dexterous industrial robots, advancing motion cueing and inverse kinematics for high-fidelity simulation. He also contributed to the study of the Uncanny Valley, exploring why near-photorealistic virtual humans can appear eerie—a paper that has garnered 25 citations for its insights into human-robot interaction. With over 100 total citations across his key works, Breidt’s research bridges simulation, perception, and robotic motion, offering practical tools and theoretical foundations for building more intuitive and realistic autonomous systems.