Conrado Ruiz

De La Salle University, Universitat Ramon Llull

Papers

Total Citations

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About

Conrado Ruiz is a researcher at the intersection of computer vision, robotics, and haptics, with a focus on enabling machines to perceive and interact with the physical world more intelligently. His work spans depth inference, shape recognition, and visuo-haptic object recognition, often leveraging generative models and virtual simulations to overcome data scarcity. In his most-cited work, "Single-Image Depth Inference Using Generative Adversarial Networks" (2019, 10 citations), Ruiz advanced the use of GANs for monocular depth estimation—a critical capability for robot grasping, obstacle avoidance, and navigation in smart environments. He further explored haptic perception through "Virtual Haptic System for Shape Recognition Based on Local Curvatures" (2021, 6 citations), demonstrating how virtual touch can aid shape identification. Most recently, in "Bridging Realities: Training Visuo-Haptic Object Recognition Models for Robots Using 3D Virtual Simulations" (2024, 4 citations), Ruiz proposed an innovative synthetic-data pipeline to train robots for multimodal object recognition, addressing the chronic shortage of labeled visuo-haptic datasets. His work is notable for bridging the gap between simulation and reality, making tangible contributions to autonomous systems and human-robot interaction.