1 citations · 1 across the 6 of their papers we have counts for
7 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…
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…
Deep Active Visual Attention for Real-time Robot Motion Generation: Emergence of Tool-body Assimilation and Adaptive Tool-use
Hyogo Hiruma, Hiroshi Ito, Hiroki Mori +1
Sufficiently perceiving the environment is a critical factor in robot motion generation. Although the introduction of deep visual processing models have contributed in extending th…
Guided Visual Attention Model Based on Interactions Between Top-down and Bottom-up Information for Robot Pose Prediction
Hyogo Hiruma, Hiroki Mori, Hiroshi Ito +1
Deep robot vision models are widely used for recognizing objects from camera images, but shows poor performance when detecting objects at untrained positions. Although such problem…