activity
20202024
most citedFoundation Model based Open Vocabulary Task Planning and Executive System for General Purpose Service Robots

3 citations · 5 across the 4 of their papers we have counts for

collaborators

6 papers

cs.RO2024

A Robot Kinematics Model Estimation Using Inertial Sensors for On-Site Building Robotics

Hiroya Sato, Tasuku Makabe, Iori Yanokura +3

In order to make robots more useful in a variety of environments, they need to be highly portable so that they can be transported to wherever they are needed, and highly storable s…

cs.RO2023

Semantic Scene Difference Detection in Daily Life Patroling by Mobile Robots using Pre-Trained Large-Scale Vision-Language Model

Yoshiki Obinata, Kento Kawaharazuka, Naoaki Kanazawa +7

It is important for daily life support robots to detect changes in their environment and perform tasks. In the field of anomaly detection in computer vision, probabilistic and deep…

cs.RO2023★ 3 cited

Foundation Model based Open Vocabulary Task Planning and Executive System for General Purpose Service Robots

Yoshiki Obinata, Naoaki Kanazawa, Kento Kawaharazuka +4

This paper describes a strategy for implementing a robotic system capable of performing General Purpose Service Robot (GPSR) tasks in robocup@home. The GPSR task is that a real rob…

cs.CV2020★ 2 cited

Understanding Action Sequences based on Video Captioning for Learning-from-Observation

Iori Yanokura, Naoki Wake, Kazuhiro Sasabuchi +2

Learning actions from human demonstration video is promising for intelligent robotic systems. Extracting the exact section and re-observing the extracted video section in detail is…

cs.RO2020

A Learning-from-Observation Framework: One-Shot Robot Teaching for Grasp-Manipulation-Release Household Operations

Naoki Wake, Riku Arakawa, Iori Yanokura +4

A household robot is expected to perform various manipulative operations with an understanding of the purpose of the task. To this end, a desirable robotic application should provi…

cs.RO2020

Verbal Focus-of-Attention System for Learning-from-Observation

Naoki Wake, Iori Yanokura, Kazuhiro Sasabuchi +1

The learning-from-observation (LfO) framework aims to map human demonstrations to a robot to reduce programming effort. To this end, an LfO system encodes a human demonstration int…