Tomoya Matsubara
Papers
1
Total Citations
5
H-Index
1
About
Tomoya Matsubara is a robotics researcher whose work focuses on enabling safe and intelligent human-robot interaction through advanced perception and estimation techniques. His primary research areas include robot control, computer vision, and deep learning for physical property estimation. Matsubara’s most notable contribution is his work on container mass estimation for human-to-robot handovers, where he developed a shared Transformer encoder architecture that simultaneously estimates filling levels and types using mask-based 3D model estimation. This innovation allows robots to safely handle objects of unknown weight and content, a critical capability for collaborative robotics. His 2022 paper on this topic has already garnered 5 citations, demonstrating early impact in the field. Matsubara’s research bridges the gap between theoretical deep learning and practical robotic applications, addressing real-world challenges in manufacturing, logistics, and domestic assistance. By focusing on the estimation of physical properties from visual data, he is helping to create robots that can more naturally and safely interact with humans and their environments.
Research Focus
Key Achievements
Top Papers
- 1