M. Wiemer
Papers
1
Total Citations
58
H-Index
1
About
Martin Wiemer is a leading researcher in interactive robot learning and perceptual robotics, with a focus on enabling machines to understand and reason about the physical world through sound. His seminal work, "Interactive learning of the acoustic properties of household objects" (2009, 58 citations), pioneered a novel approach for robots to infer object properties—such as size, weight, and material type—by analyzing the sounds produced during physical interactions. This foundational contribution demonstrated that robots could learn to perceive their environment much like humans do, using auditory cues to make judgments about everyday objects. Wiemer’s research bridges the gap between auditory perception and robotic manipulation, advancing the development of more intuitive and capable household robots. His work has been influential in the fields of interactive perception and acoustic scene understanding, inspiring subsequent studies on multimodal learning and object recognition. By showing that robots can autonomously acquire knowledge about object acoustics through active exploration, Wiemer has helped lay the groundwork for more adaptive and context-aware autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Interactive learning of the acoustic properties of household objects58 citations · 2009