Thomas Killus
Papers
1
Total Citations
6
H-Index
1
About
Thomas Killus is a robotics researcher specializing in computer vision and autonomous manipulation, with a focus on streamlining data acquisition for deep learning systems. His most-cited work, "Automatic data collection for object detection and grasp-position estimation with mobile robots and invisible markers" (2022, 6 citations), addresses a critical bottleneck in robotics: the labor-intensive process of gathering training data for convolutional neural networks. By introducing a method that leverages invisible markers on mobile robots to automatically collect and annotate visual data, Killus reduces the time and cost of building robust object detection and grasp estimation models. This contribution is particularly valuable for real-world applications where manual data labeling is impractical. While his citation count is still growing, his work represents a practical step toward scalable, autonomous robotic learning. Killus’s research sits at the intersection of robotics, computer vision, and efficient machine learning pipelines, offering a foundation for future advances in automated data collection for manipulation tasks.
Research Focus
Key Achievements
Top Papers
- 1