8 papers
FRAM: Trajectory-Guided Visual Feature Selection for Compact Language-Conditioned Robot Manipulation
Hiroshi Ito, Hyogo Hiruma, Yoshiki Kanai +4
Vision-Language-Action models achieve strong performance in robot manipulation, but often require large numbers of parameters. In this work, we propose the Future Representation Ac…
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…
A3RNN: Bi-directional Fusion of Bottom-up and Top-down Process for Developmental Visual Attention in Robots
Hyogo Hiruma, Hiroshi Ito, Hiroki Mori +1
This study investigates the developmental interaction between top-down (TD) and bottom-up (BU) visual attention in robotic learning. Our goal is to understand how structured, human…
UF-RNN: Real-Time Adaptive Motion Generation Using Uncertainty-Driven Foresight Prediction
Hyogo Hiruma, Hiroshi Ito, Tetsuya Ogata
Training robots to operate effectively in environments with uncertain states, such as ambiguous object properties or unpredictable interactions, remains a longstanding challenge in…
AIRoA MoMa Dataset: A Large-Scale Hierarchical Dataset for Mobile Manipulation
Ryosuke Takanami, Petr Khrapchenkov, Shu Morikuni +32
As robots transition from controlled settings to unstructured human environments, building generalist agents that can reliably follow natural language instructions remains a centra…
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…
Adaptive Motion Generation Using Uncertainty-Driven Foresight Prediction
Hyogo Hiruma, Hiroshi Ito, Tetusya Ogata
Uncertainty of environments has long been a difficult characteristic to handle, when performing real-world robot tasks. This is because the uncertainty produces unexpected observat…
Achieving Faster and More Accurate Operation of Deep Predictive Learning
Masaki Yoshikawa, Hiroshi Ito, Tetsuya Ogata
Achieving both high speed and precision in robot operations is a significant challenge for social implementation. While factory robots excel at predefined tasks, they struggle with…