Tomoya Yamanokuchi — accepted AI-conference papers

2024

Domain Randomization-free Sim-to-Real : An Attention-Augmented Memory Approach for Robotic Tasks

Jia Qu, Shun Otsubo, Tomoya Yamanokuchi, Takamitsu Matsubara, Shotaro Miwa

IROS 2024poster

The sim-to-real gap, a long-standing challenge in the field of robotics, has garnered significant attention. Essentially, it is important to learn robust representation models that can be seamlessly applied in both simulation and real world. Traditional approaches like domain randomization have demo…

Cited by 0 Source ↗Scholar ↗

2022

Randomized-to-Canonical Model Predictive Control for Real-World Visual Robotic Manipulation

Tomoya Yamanokuchi, Yuhwan Kwon, Yoshihisa Tsurumine, Eiji Uchibe, Jun Morimoto, Takamitsu Matsubara

RA-L 2022

Many works have recently explored Sim-to-real transferable visual model predictive control (MPC). However, such works are limited to one-shot transfer, where real-world data must be collected once to perform the sim-to-real transfer, which remains a significant human effort in transferring the model

Cited by 5 Source ↗Scholar ↗