95 citations · 173 across the 18 of their papers we have counts for
19 papers
Robotic Stroke Motion Following the Shape of the Human Back: Motion Generation and Psychological Effects
Akishige Yuguchi, Tomoki Ishikura, Sung-Gwi Cho +2
In this study, to perform the robotic stroke motions following the shape of the human back similar to the stroke motions by humans, in contrast to the conventional robotic stroke m…
GPT Models Meet Robotic Applications: Co-Speech Gesturing Chat System
Naoki Wake, Atsushi Kanehira, Kazuhiro Sasabuchi +2
This technical paper introduces a chatting robot system that utilizes recent advancements in large-scale language models (LLMs) such as GPT-3 and ChatGPT. The system is integrated…
Applying Learning-from-observation to household service robots: three common-sense formulation
Katsushi Ikeuchi, Jun Takamatsu, Kazuhiro Sasabuchi +2
Utilizing a robot in a new application requires the robot to be programmed at each time. To reduce such programmings efforts, we have been developing ``Learning-from-observation (L…
Efficiently Collecting Training Dataset for 2D Object Detection by Online Visual Feedback
Takuya Kiyokawa, Naoki Shirakura, Hiroki Katayama +2
Training deep-learning-based vision systems require the manual annotation of a significant number of images. Such manual annotation is highly time-consuming and labor-intensive. Al…
ChatGPT Empowered Long-Step Robot Control in Various Environments: A Case Application
Naoki Wake, Atsushi Kanehira, Kazuhiro Sasabuchi +2
This paper demonstrates how OpenAI's ChatGPT can be used in a few-shot setting to convert natural language instructions into a sequence of executable robot actions. The paper propo…
Task-sequencing Simulator: Integrated Machine Learning to Execution Simulation for Robot Manipulation
Kazuhiro Sasabuchi, Daichi Saito, Atsushi Kanehira +3
A task-sequencing simulator in robotics manipulation to integrate simulation-for-learning and simulation-for-execution is introduced. Unlike existing machine-learning simulation wh…
Interactive Task Encoding System for Learning-from-Observation
Naoki Wake, Atsushi Kanehira, Kazuhiro Sasabuchi +2
We present the Interactive Task Encoding System (ITES) for teaching robots to perform manipulative tasks. ITES is designed as an input system for the Learning-from-Observation (LfO…
Design strategies for controlling neuron-connected robots using reinforcement learning
Haruto Sawada, Naoki Wake, Kazuhiro Sasabuchi +3
Despite the growing interest in robot control utilizing the computation of biological neurons, context-dependent behavior by neuron-connected robots remains a challenge. Context-de…
Task-grasping from human demonstration
Daichi Saito, Kazuhiro Sasabuchi, Naoki Wake +3
A challenge in robot grasping is to achieve task-grasping which is to select a grasp that is advantageous to the success of tasks before and after grasps. One of the frameworks to…
Active Vapor-Based Robotic Wiper
Takuya Kiyokawa, Hiroki Katayama, Jun Takamatsu +1
This paper presents a method for estimating normals of mirrors and transparent objects challenging for cameras to recognize. We propose spraying water vapor onto mirror or transpar…
Soft-Jig: A Flexible Sensing Jig for Simultaneously Fixing and Estimating Orientation of Assembly Parts
Tatsuya Sakuma, Takuya Kiyokawa, Jun Takamatsu +2
For assembly tasks, it is essential to firmly fix target parts and to accurately estimate their poses. Several rigid jigs for individual parts are frequently used in assembly facto…
Robotic Waste Sorter with Agile Manipulation and Quickly Trainable Detector
Takuya Kiyokawa, Hiroki Katayama, Yuya Tatsuta +2
Owing to human labor shortages, the automation of labor-intensive manual waste-sorting is needed. The goal of automating waste-sorting is to replace the human role of robust detect…
Semantic constraints to represent common sense required in household actions for multi-modal Learning-from-observation robot
Katsushi Ikeuchi, Naoki Wake, Riku Arakawa +2
The paradigm of learning-from-observation (LfO) enables a robot to learn how to perform actions by observing human-demonstrated actions. Previous research in LfO have mainly focuse…
Toward an Affective Touch Robot: Subjective and Physiological Evaluation of Gentle Stroke Motion Using a Human-Imitation Hand
Tomoki Ishikura, Akishige Yuguchi, Yuki Kitamura +6
Affective touch offers positive psychological and physiological benefits such as the mitigation of stress and pain. If a robot could realize human-like affective touch, it would op…
Assembly Sequences Based on Multiple Criteria Against Products with Deformable Parts
Takuya Kiyokawa, Jun Takamatsu, Tsukasa Ogasawara
Aiming to generate easy-to-handle assembly sequences for robotic assembly, this study tackles assembly sequence generation by considering two tradeoff objectives: (1) insertion con…
Soft-Jig-Driven Assembly Operations
Takuya Kiyokawa, Tatsuya Sakuma, Jun Takamatsu +1
To design a general-purpose assembly robot system that can handle objects of various shapes, we propose a soft jig that fits to the shapes of assembly parts. The functionality of t…
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…
Control of Walking Assist Exoskeleton with Time-delay Based on the Prediction of Plantar Force
Ming Ding, Mikihisa Nagashima, Sung-Gwi Cho +2
Many kinds of lower-limb exoskeletons were developed for walking assistance. However, when controlling these exoskeletons, time-delay due to the computation time and the communicat…
Multi-View Inpainting for RGB-D Sequence
Feiran Li, Gustavo Alfonso Garcia Ricardez, Jun Takamatsu +1
In this work we propose a novel approach to remove undesired objects from RGB-D sequences captured with freely moving cameras, which enables static 3D reconstruction. Our method jo…