Ines Ugalde
Papers
1
Total Citations
2
H-Index
1
About
Ines Ugalde is a robotics researcher whose work bridges the gap between cutting-edge deep learning and industrial automation. Her primary research areas include robotic grasping, computer vision, and the integration of artificial intelligence with programmable logic controllers (PLCs) for manufacturing. Ugalde’s most notable contribution is her 2020 paper, "Industrial Robot Grasping with Deep Learning using a Programmable Logic Controller (PLC)," which tackles the grand challenge of enabling robots to universally grasp a diverse range of previously unseen objects from heaps—a critical need for e-commerce order fulfillment and home service robotics. By fusing deep learning-based grasping algorithms with traditional PLC control systems, she demonstrates how AI can be practically deployed in real-world factory settings. While her citation count is still growing, her work represents a vital step toward making intelligent, flexible automation accessible to industry. Ugalde’s research is particularly valuable for students and engineers seeking to understand how to implement state-of-the-art perception and manipulation techniques within the constraints of existing industrial hardware.
Research Focus
Key Achievements
Top Papers
- 1