Tatsuya Kamijo
Papers
1
Total Citations
13
H-Index
1
About
Tatsuya Kamijo is an emerging robotics researcher whose work sits at the intersection of robot learning, compliance control, and teleoperation systems. His most recognized contribution focuses on a fundamental challenge in modern robotics: enabling rigid robots to perform dexterous, contact-rich manipulation tasks safely and effectively. In his standout 2024 paper, which has already garnered 13 citations, Kamijo tackles the problem of excessive contact forces in rigid robots by developing a framework that learns variable compliance control from only a small number of demonstrations. This work is particularly significant because it leverages haptic feedback teleoperation to capture nuanced human manipulation strategies, allowing bimanual robotic systems to adapt intelligently to environmental contact — a capability previously difficult to achieve without extensive training data. By bridging the gap between human dexterity and robotic execution, Kamijo's research addresses real-world deployment concerns in manufacturing, assistive robotics, and human-robot collaboration. His ability to extract meaningful learned behaviors from minimal demonstrations reflects a broader trend toward data-efficient robot learning, positioning his work as a valuable contribution for researchers striving to make capable robots more practical and accessible in complex physical environments.
Research Focus
Key Achievements
Top Papers
- 1
Key Collaborators
Related papers
- Learning Variable Compliance Control From a Few Demonstrations for Bimanual Robot with Haptic Feedback Teleoperation System
- Learning Variable Compliance Control From a Few Demonstrations for Bimanual Robot with Haptic Feedback Teleoperation System
- Learning Diffusion Policies from Demonstrations For Compliant Contact-rich Manipulation
- Learning Compliant Manipulation through Kinesthetic and Tactile Human-Robot Interaction
- Learning Compliant Manipulation Tasks from Force Demonstrations
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