Papers
2
Total Citations
89
H-Index
2
About
Joel Vidal is a leading researcher in robotics and computer vision, with a focus on bridging the gap between automated manufacturing and intelligent machine perception. His work centers on 3D vision, deep learning, and robotic manipulation, particularly in the areas of object grasping and autonomous path planning. Vidal’s most influential contributions include the development of a novel system for automatic robot path integration using three-dimensional vision and offline programming, a paper that has garnered 54 citations and laid the groundwork for more flexible industrial automation. He further advanced the field with a 2021 study on 6D pose estimation, combining deep learning with 3D vision techniques to enable fast and accurate object grasping—a work cited 35 times for its practical impact on real-time robotic interaction. Vidal’s research is notable for its emphasis on real-world applicability, integrating cutting-edge neural networks with classical computer vision to solve complex manipulation tasks. His achievements highlight a commitment to making robots more autonomous and efficient in unstructured environments, positioning him as a key contributor to the next generation of intelligent manufacturing systems.
Research Focus
Key Achievements
Top Papers
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