Ferran Rigual
Papers
1
Total Citations
9
H-Index
1
About
Ferran Rigual is a researcher whose work sits at the intersection of computer vision and service robotics, with a particular focus on enabling robots to perceive and interact with their environment. His most cited contribution, "Object detection methods for robot grasping: Experimental assessment and tuning" (2012, 9 citations), addresses a critical bottleneck in autonomous manipulation: reliably detecting objects for grasping. Rather than proposing a single novel algorithm, Rigual’s work provides a rigorous, practical evaluation of multiple state-of-the-art detection methods, systematically tuning and comparing them in a realistic service robotics scenario. This experimental assessment offers valuable guidance for practitioners, highlighting which methods perform best under real-world constraints and identifying key practical considerations for deployment. By bridging the gap between theoretical computer vision advances and the tangible needs of robotic grasping, Rigual’s research helps lay the groundwork for more capable and reliable service robots. His work is particularly relevant for students and engineers seeking to understand how to select and optimize object detection pipelines for real robotic systems, making his contributions a useful reference in the applied robotics community.
Research Focus
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Top Papers
- 1