Yuto Uchimi
Papers
1
Total Citations
22
H-Index
1
About
Yuto Uchimi is a robotics researcher whose work sits at the intersection of computer vision, manipulation, and deep learning. His primary research focus is on enabling robots to perform complex, dexterous motions by learning and fusing multiple grasp modalities—such as pinch and suction—with instance segmentation. Uchimi’s key contribution is developing frameworks that allow multi-modal grippers to intelligently select and execute the most appropriate grasp strategy for novel objects, moving beyond single-modality approaches. His most cited work, “GraspFusion” (2019), has garnered 22 citations and demonstrates how deep learning can unify grasp planning and object recognition in real-time. This research is foundational for advancing robotic autonomy in unstructured environments, such as warehouses or homes, where objects vary widely in shape, texture, and fragility. Uchimi’s work is particularly notable for its practical impact on multi-modal gripper design, bridging the gap between perception and action. As a rising figure in robotic manipulation, his contributions are shaping how robots interact with the physical world, making them more adaptive and capable.
Research Focus
Key Achievements
Top Papers
- 1