8 citations · 14 across the 5 of their papers we have counts for
9 papers · 1 filter
VLM-driven Behavior Tree for Context-aware Task Planning
Naoki Wake, Atsushi Kanehira, Jun Takamatsu +2
The use of Large Language Models (LLMs) for generating Behavior Trees (BTs) has recently gained attention in the robotics community, yet remains in its early stages of development.…
Deep Gesture Generation for Social Robots Using Type-Specific Libraries
Hitoshi Teshima, Naoki Wake, Diego Thomas +3
Body language such as conversational gesture is a powerful way to ease communication. Conversational gestures do not only make a speech more lively but also contain semantic meanin…
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…
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…