Makito Seki
Papers
1
Total Citations
2
H-Index
1
About
Makito Seki is a researcher in robotics and computer vision, with a focus on enabling robots to interact with their environments through precise manipulation. His work centers on grasp detection for robotic systems, particularly using deep learning approaches to improve how robots handle industrial parts and everyday objects. Seki’s most-cited paper, "Grasping Detection using Deep Convolutional Neural Network with Graspability" (2018), introduces a method that leverages convolutional neural networks to accurately identify grasp points, addressing a critical challenge in both industrial automation and assistive robotics. Although his citation count is still growing—with this key work garnering 2 citations—his research contributes to the foundational goal of making robots more autonomous and capable in unstructured settings. By combining deep learning with graspability metrics, Seki advances the practical deployment of robots in real-world tasks, from manufacturing to home assistance. His work reflects a commitment to bridging the gap between machine learning theory and tangible robotic applications, promising future impact as the field evolves.
Research Focus
Key Achievements
Top Papers
- 1