Tomoya Yamanokuchi
Papers
1
Total Citations
5
H-Index
1
About
Tomoya Yamanokuchi is a robotics researcher whose work focuses on bridging the gap between simulation and real-world robotic manipulation through advanced model predictive control (MPC). His key contributions lie in developing randomized-to-canonical approaches that enable visual robotic systems to transfer skills from simulated environments to physical hardware with minimal human intervention. His most cited work, "Randomized-to-Canonical Model Predictive Control for Real-World Visual Robotic Manipulation" (2022), addresses a critical bottleneck in sim-to-real transfer by reducing the need for extensive real-world data collection—a persistent challenge that has limited the practical deployment of learning-based control. By proposing methods that eliminate the one-shot data collection requirement, Yamanokuchi’s research directly tackles the human effort barrier in model transfer, making robotic manipulation more scalable and accessible. His work has garnered attention within the robotics community, with his top-cited paper accumulating 5 citations, reflecting its relevance to researchers seeking more efficient sim-to-real pipelines. Yamanokuchi’s contributions are particularly notable for their potential to accelerate the deployment of visual robotic systems in unstructured real-world environments, advancing the frontier of autonomous manipulation.
Research Focus
Key Achievements
Top Papers
- 1