Masato Kobayashi — accepted AI-conference papers

2025

DABI: Evaluation of Data Augmentation Methods Using Downsampling in Bilateral Control-Based Imitation Learning with Images

Masato Kobayashi, Thanpimon Buamanee, Yuki Uranishi

ICRA 2025

Autonomous robot manipulation is a complex and continuously evolving robotics field. This paper focuses on data augmentation methods in imitation learning. Imitation learning consists of three stages: data collection from experts, learning model, and execution. However, collecting expert data requir

Cited by 6 Source ↗code ↗Scholar ↗

2024

GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware Policy

So Kuroki, Jiaxian Guo, Tatsuya Matsushima, Takuya Okubo, Masato Kobayashi, Yuya Ikeda +4

ICRA 2024poster

Due to the inherent uncertainty in their deformability during motion, previous methods in deformable object manipulation, such as rope and cloth, often required hundreds of real-world demonstrations to train a manipulation policy for each object, which hinders their applications in our ever-changing…

Cited by 2 Source ↗Scholar ↗