2 citations · 4 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
GenORM: Generalizable One-shot Rope 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 rope manipulation often require hundreds of real-world demonstrations to train a manipulat…