Friedhelm Schwenker
Papers
9
Total Citations
89
H-Index
5
About
Friedhelm Schwenker is a leading figure in machine learning and neural computation, whose work bridges the gap between cognitive robotics and multimodal human-computer interaction. His research centers on developing intelligent systems that perceive, learn, and interact with their environment, with major contributions in object recognition for autonomous robots and affective computing. In the early 2000s, Schwenker pioneered hierarchical neural network architectures—such as Radial Basis Function networks and support vector classifiers—for 3D visual object recognition, enabling mobile robots to localize and identify task-relevant objects through color-based attention control and multi-level spatial representations. His work on concurrent object identification and localization (2000, 12 citations) and learning object recognition in a neurobotic system (2004, 17 citations) laid foundational algorithms for service robotics. Later, Schwenker expanded into multimodal interaction, notably with his study on laughter detection in natural discourses (2009, 31 citations), which advanced emotion-aware computing. He has also explored meta-learning and attention-gated reinforcement learning, contributing to adaptive neural network training. With over 80 citations across his most influential papers, Schwenker’s research continues to inspire innovations in autonomous systems and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1Multimodal Laughter Detection in Natural Discourses31 citations · 2009
- 2Learning Object Recognition in a NeuroBotic System17 citations · 2004
- 3Concurrent Object Identification and Localization for a Mobile Robot.12 citations · 2000
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- 5Hierarchical Object Classification for Autonomous Mobile Robots6 citations · 2002
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- 7Sensor-Fusion in Neural Networks4 citations · 2009
- 8Attention-Gated Reinforcement Learning in Neural Networks—A Unified View4 citations · 2013
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