Shunya Nagashima
Papers
1
Total Citations
7
H-Index
1
About
Shunya Nagashima is advancing the frontier of domestic service robotics through a human-in-the-loop approach, with a particular focus on enabling robots to identify and retrieve everyday objects autonomously. His most cited work, "Learning-To-Rank Approach for Identifying Everyday Objects Using a Physical-World Search Engine" (2024, 7 citations), addresses a critical bottleneck in real-world robot deployment: the challenge of locating target objects in cluttered, dynamic home environments. By framing object identification as a learning-to-rank problem, Nagashima integrates automation with operator oversight, creating a system that balances efficiency with reliability—a pragmatic solution for scaling assistive robots in society. This work is foundational for developing robots that can support daily care tasks, reducing the burden on human caregivers. Though early in his career, Nagashima’s research has already garnered attention for its practical, human-centered design, positioning him as a promising voice in the intersection of robotics, machine learning, and human-robot interaction. His contributions are particularly relevant for researchers and students interested in building robust, real-world robotic systems that can operate safely alongside people.
Research Focus
Key Achievements
Top Papers
- 1