Tri Wahyu Utomo
Papers
1
Total Citations
14
H-Index
1
About
Tri Wahyu Utomo is a robotics researcher specializing in robotic manipulation and deep learning for autonomous grasping in cluttered environments. His work focuses on developing intelligent systems that enable robotic manipulators to perceive and interact with complex, unstructured surroundings. His most-cited paper, "Suction-based Grasp Point Estimation in Cluttered Environment for Robotic Manipulator Using Deep Learning-based Affordance Map" (2021), has garnered 14 citations, highlighting its influence in the field of robotic perception and manipulation. In this work, Utomo introduces a novel approach that leverages deep learning to generate affordance maps, allowing robots to estimate optimal suction grasp points even in heavily cluttered settings—a critical advancement for applications in warehouse automation, manufacturing, and service robotics. His contributions bridge the gap between computer vision and robotic control, offering practical solutions for real-world deployment. Utomo’s research is notable for its emphasis on robust, real-time performance, making his methods valuable for both academic study and industrial implementation. As a rising voice in robotics, his work continues to inspire further exploration into learning-based grasp planning and affordance reasoning.
Research Focus
Key Achievements
Top Papers
- 1