Syler Wagner

Papers

Total Citations

156

H-Index

About

Syler Wagner is a roboticist whose research lies at the intersection of computer vision and autonomous manipulation, with a focus on enabling robots to perceive and interact with unstructured environments. His most influential contribution, SegICP (2017, 151 citations), pioneered an integrated approach combining deep semantic segmentation with pose estimation, addressing a critical bottleneck in robotic perception: the ability to rapidly and reliably identify and localize objects in complex, real-world scenarios. This work directly responded to challenges highlighted in robotic manipulation competitions, where speed and robustness often falter. Wagner further advanced the field with SegICP-DSR, a real-time system for dense semantic scene reconstruction and registration that achieves impressive mm-level pose accuracy (7.9 mm, σ=7.6 mm) and angular precision (1.7 deg, σ=0.7 deg). This extension demonstrates his commitment to pushing perception systems from isolated object recognition toward holistic scene understanding. By tackling the gap between laboratory demonstrations and practical deployment, Wagner’s research has provided foundational tools for robots operating in cluttered, unpredictable settings—work that continues to influence the design of perception pipelines in manipulation, service robotics, and autonomous systems.