Simon Haller-Seeber
Papers
5
Total Citations
27
H-Index
3
About
Simon Haller-Seeber is a researcher at the intersection of artificial intelligence, robotics, and education, with a primary focus on advancing autonomous robotic manipulation and industrial automation. His most impactful work, "A Visual Intelligence Scheme for Hard Drive Disassembly in Automated Recycling Routines" (16 citations), introduces a deep learning framework for visual scene analysis that enables robots to perform complex disassembly tasks—a critical step toward sustainable e-waste recycling. Haller-Seeber has also made notable contributions to affordance detection, proposing a novel Dynamic-Tree Capsule Network that improves how robots understand object interaction possibilities by preserving spatial hierarchies, a departure from traditional convolutional approaches. Beyond technical research, he is a driving force in STEM outreach as the co-creator of the Software Testing, AI and Robotics (STAIR) Learning Lab at the University of Innsbruck, which brings physical and virtual robotics curricula into schools. This dual commitment to cutting-edge manipulation research and accessible education underscores his vision of robotics as both a tool for industrial efficiency and a platform for inspiring the next generation of engineers.
Research Focus
Key Achievements
Top Papers
- 1
- 2Software Testing, AI and Robotics (STAIR) Learning Lab4 citations · 2022
- 3Software Testing, AI and Robotics (STAIR) Learning Lab3 citations · 2022
- 4Affordance detection with Dynamic-Tree Capsule Networks2 citations · 2022
- 5A Block–based IDE Extension for the ESP322 citations · 2021