3 papers
cs.RO2026
Exploration-assisted Bottleneck Transition Toward Robust and Data-efficient Deformable Object Manipulation
Yujiro Onishi, Ryo Takizawa, Yoshiyuki Ohmura +1
Imitation learning has demonstrated impressive results in robotic manipulation but fails under out-of-distribution (OOD) states. This limitation is particularly critical in Deforma…
cs.RO2025
Enhancing Reusability of Learned Skills for Robot Manipulation via Gaze Information and Motion Bottlenecks
Ryo Takizawa, Izumi Karino, Koki Nakagawa +2
Autonomous agents capable of diverse object manipulations should be able to acquire a wide range of manipulation skills with high reusability. Although advances in deep learning ha…
cs.RO2025
Gaze-Guided Task Decomposition for Imitation Learning in Robotic Manipulation
Ryo Takizawa, Yoshiyuki Ohmura, Yasuo Kuniyoshi
In imitation learning for robotic manipulation, decomposing object manipulation tasks into sub-tasks enables the reuse of learned skills and the combination of learned behaviors to…