4 papers
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…
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…
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…