6 papers · 1 filter
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…
Imitation Learning for Active Neck Motion Enabling Robot Manipulation beyond the Field of View
Koki Nakagawa, Yoshiyuki Ohmura, Yasuo Kuniyoshi
Most prior research in deep imitation learning has predominantly utilized fixed cameras for image input, which constrains task performance to the predefined field of view. However,…
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…
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…
Memory-based gaze prediction in deep imitation learning for robot manipulation
Heecheol Kim, Yoshiyuki Ohmura, Yasuo Kuniyoshi
Deep imitation learning is a promising approach that does not require hard-coded control rules in autonomous robot manipulation. The current applications of deep imitation learning…
Third-party Evaluation of Robotic Hand Designs Using a Mechanical Glove
Takayuki Kanai, Yoshiyuki Ohmura, Akihiko Nagakubo +1
A robotic hand design suitable for dexterity should be examined using functional tests. To achieve this, we designed a mechanical glove, which is a rigid wearable glove that enable…