Papers
2
Total Citations
4
H-Index
1
About
Simon Reichhuber is a rising researcher in the field of robotic manipulation and computer vision, with a focus on occlusion-aware systems. His work addresses a critical challenge in highly automated environments: ensuring that robotic manipulators remain visible to optical monitoring cameras, which are essential for precise, real-time control. Reichhuber’s key contributions include developing learning-based methods to predict and prevent self-occlusions, as demonstrated in his 2024 paper, “Learning Occlusions in Robotic Systems: How to Prevent Robots from Hiding Themselves” (3 citations). He further advanced this area with his 2025 work, “Occlusion Avoidance for Robotic Manipulators Using Rigid Gaussian Splatting” (1 citation), which introduces a lightweight, efficient solution that leverages rigid Gaussian splatting to maintain line-of-sight without heavy computational overhead. Though early in his career, Reichhuber’s research is impactful for its practical application in industrial settings, where reliable camera-based monitoring is crucial for safety and automation. His work bridges the gap between perception and control, offering a scalable approach to occlusion avoidance that could enhance the robustness of robotic systems in real-world environments.
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Top Papers
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