Victoria Colthurst

Papers

Total Citations

H-Index

About

Victoria Colthurst is a robotics researcher whose work centers on autonomous perception and 3D object modeling for robotic manipulation. Her primary contributions lie in developing self-contained hardware-software pipelines that enable robotic arms to autonomously acquire and reconstruct 3D object models without human intervention. In her most-cited work, "Autonomous Object Model Acquisition with a Robotic Arm" (2023), Colthurst introduces a complete registration system that integrates a camera and robotic arm to generate high-fidelity 3D point clouds. Through rigorous quantitative and qualitative evaluation, she demonstrates that the system produces accurate, usable object models—a critical step toward enabling robots to interact with unfamiliar objects in unstructured environments. While her citation count is still growing, the foundational nature of this work positions her as an emerging voice in autonomous robotics. Colthurst's research addresses a key bottleneck in robotic learning: the need for scalable, automatic object model acquisition. Her approach promises to accelerate progress in areas from warehouse automation to assistive robotics, where robots must adapt to new objects on the fly.