8 citations · 14 across the 4 of their papers we have counts for
7 papers
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…
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…
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…
Grasp-type Recognition Leveraging Object Affordance
Naoki Wake, Kazuhiro Sasabuchi, Katsushi Ikeuchi
A key challenge in robot teaching is grasp-type recognition with a single RGB image and a target object name. Here, we propose a simple yet effective pipeline to enhance learning-b…
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…