1 citations · 2 across the 4 of their papers we have counts for
5 papers
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…
SPARK: Graph-Based Online Semantic Integration System for Robot Task Planning
Mimo Shirasaka, Yuya Ikeda, Tatsuya Matsushima +2
The ability to update information acquired through various means online during task execution is crucial for a general-purpose service robot. This information includes geometric an…
TRAIL Team Description Paper for RoboCup@Home 2023
Chikaha Tsuji, Dai Komukai, Mimo Shirasaka +13
Our team, TRAIL, consists of AI/ML laboratory members from The University of Tokyo. We leverage our extensive research experience in state-of-the-art machine learning to build gene…
Self-Recovery Prompting: Promptable General Purpose Service Robot System with Foundation Models and Self-Recovery
Mimo Shirasaka, Tatsuya Matsushima, Soshi Tsunashima +8
A general-purpose service robot (GPSR), which can execute diverse tasks in various environments, requires a system with high generalizability and adaptability to tasks and environm…
GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware Policy
So Kuroki, Jiaxian Guo, Tatsuya Matsushima +7
Due to the inherent uncertainty in their deformability during motion, previous methods in deformable object manipulation, such as rope and cloth, often required hundreds of real-wo…