38 citations · 49 across the 7 of their papers we have counts for
9 papers
Hardware-as-Regularization: Enhancing Data Consistency in Imitation Learning via an Adjustable Passive Compliant Leader Arm
Hideyuki Ichiwara, Yushi Kaida, Hirokazu Ishida
Low-cost passive leader arms enable scalable data collection for imitation learning, but their lack of restoring forces leaves redundant joints unconstrained, yielding highly incon…
TLC-DiT: Task-Aligned Local Visual Conditioning for Robust Multitask Robot Manipulation
Xianbo Cai, Hideyuki Ichiwara, Zihang Wang +2
Language-conditioned robot policies have made clear progress in multitask manipulation, but task-relevant local visual evidence usually stays hidden inside a visual backbone or att…
MSACT: Multistage Spatial Alignment for Stable Low-Latency Fine Manipulation
Xianbo Cai, Hideyuki Ichiwara, Masaki Yoshikawa +1
Real-world fine manipulation, particularly in bimanual manipulation, typically requires low-latency control and stable visual localization, while collecting large-scale data is cos…
Stereo Multistage Spatial Attention for Real-Time Mobile Manipulation Under Visual Scale Variation and Disturbances
Xianbo Cai, Hideyuki Ichiwara, Hyogo Hiruma +3
Robots operating in open, unstructured real-world environments must rely on onboard visual perception while autonomously moving across different locations. Continuous changes in on…
Input-gated Bilateral Teleoperation: An Easy-to-implement Force Feedback Teleoperation Method for Low-cost Hardware
Yoshiki Kanai, Akira Kanazawa, Hideyuki Ichiwara +3
Effective data collection in contact-rich manipulation requires force feedback during teleoperation, as accurate perception of contact is crucial for stable control. However, such…
Visual Spatial Attention and Proprioceptive Data-Driven Reinforcement Learning for Robust Peg-in-Hole Task Under Variable Conditions
André Yuji Yasutomi, Hideyuki Ichiwara, Hiroshi Ito +2
Anchor-bolt insertion is a peg-in-hole task performed in the construction field for holes in concrete. Efforts have been made to automate this task, but the variable lighting and h…
Real-time Motion Generation and Data Augmentation for Grasping Moving Objects with Dynamic Speed and Position Changes
Kenjiro Yamamoto, Hiroshi Ito, Hideyuki Ichiwara +2
While deep learning enables real robots to perform complex tasks had been difficult to implement in the past, the challenge is the enormous amount of trial-and-error and motion tea…
Contact-Rich Manipulation of a Flexible Object based on Deep Predictive Learning using Vision and Tactility
Hideyuki Ichiwara, Hiroshi Ito, Kenjiro Yamamoto +2
We achieved contact-rich flexible object manipulation, which was difficult to control with vision alone. In the unzipping task we chose as a validation task, the gripper grasps the…
Spatial Attention Point Network for Deep-learning-based Robust Autonomous Robot Motion Generation
Hideyuki Ichiwara, Hiroshi Ito, Kenjiro Yamamoto +2
Deep learning provides a powerful framework for automated acquisition of complex robotic motions. However, despite a certain degree of generalization, the need for vast amounts of…