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About

Tobias Rodemann is a researcher specializing in robot audition, spatial sound processing, and human-robot interaction, with a particular focus on enabling humanoid robots to perceive and interpret their acoustic environments more effectively. His most influential contribution lies in tackling the challenging problem of ego noise — the self-generated motor and joint noise that plagues humanoid robots — developing both template subtraction and hybrid cancellation frameworks that substantially improve automatic speech recognition during robot motion. His 2009 paper on ego noise suppression has accumulated 52 citations, establishing him as a leading voice in this niche but critical area. Beyond noise cancellation, Rodemann has advanced binaural sound localization, including underexplored dimensions such as distance estimation, and introduced the concept of "audio proto objects" — compact multi-feature representations that refine sound localization precision. His work also extends into cognitive robotics, notably through ASIMO-integrated systems capable of multimodal association learning and headset-free speech interaction, drawing inspiration from infant developmental processes. With incremental and reward-based learning approaches further rounding out his portfolio, Rodemann's research collectively addresses the full pipeline of making humanoid robots genuinely capable listeners and learners in real-world environments.