Iori Yanaokura
Papers
1
Total Citations
10
H-Index
1
About
Iori Yanaokura is a robotics researcher whose work centers on making robot teaching more intuitive and efficient through multimodal learning-from-observation (LfO). His key research areas include human-robot interaction, task cohesion, and the integration of language and demonstration for seamless robot instruction. Yanaokura’s major contribution is advancing all-at-once robot teaching, a paradigm that allows users to convey entire sequential operations in a single interaction, rather than step-by-step. By extracting “what-to-do” from natural language and “how-to-do” from physical demonstrations, his approach bridges the gap between human communication and robotic execution. His most-cited paper, “A Multimodal Learning-from-Observation Towards All-at-once Robot Teaching using Task Cohesion” (2022, 10 citations), lays the groundwork for this method, demonstrating how task cohesion can unify multimodal inputs for more fluid teaching. Though early in his career, Yanaokura’s work has already attracted attention for its potential to democratize robot programming, reducing the need for expert coding. His research promises to transform how non-specialists interact with robots, making automation more accessible in manufacturing, healthcare, and domestic settings.
Research Focus
Key Achievements
Top Papers
- 1