Ryota Takamido

2 accepted papers

2025

Experience-based Optimal Motion Planning Algorithm for Solving Difficult Planning Problems Using a Limited Dataset

RA-L 2025

This study addresses the challenge of generating high-quality motion plans within a short computation time using only a limited dataset. In the informed experience-driven random trees connect star (IERTC*) process, the algorithm flexibly explores the search trees by morphing the micro paths generate

Cited by 2 Source ↗Scholar ↗

2023

Learning Robot Motion in a Cluttered Environment Using Unreliable Human Skeleton Data Collected by a Single RGB Camera

Ryota Takamido, Jun Ota

RA-L 2023

Existing learning from demonstration (LfD) frameworks have difficulty dealing with unreliable and limited number of demonstrations. To address this issue, we proposed a new motion planning framework named experience-driven random tree connect with human demonstration (ERTC-HD), which can facilitate

Cited by 3 Source ↗Scholar ↗