activity
20162024
most citedAgent AI: Surveying the Horizons of Multimodal Interaction

48 citations · 50 across the 7 of their papers we have counts for

collaborators

7 papers

cs.RO2024

Designing Library of Skill-Agents for Hardware-Level Reusability

Jun Takamatsu, Daichi Saito, Katsushi Ikeuchi +3

To use new robot hardware in a new environment, it is necessary to develop a control program tailored to that specific robot in that environment. Considering the reusability of sof…

cs.AI202448 cited

Agent AI: Surveying the Horizons of Multimodal Interaction

Zane Durante, Qiuyuan Huang, Naoki Wake +11

Multi-modal AI systems will likely become a ubiquitous presence in our everyday lives. A promising approach to making these systems more interactive is to embody them as agents wit…

cs.RO2023

Constraint-aware Policy for Compliant Manipulation

Daichi Saito, Kazuhiro Sasabuchi, Naoki Wake +4

Robot manipulation in a physically-constrained environment requires compliant manipulation. Compliant manipulation is a manipulation skill to adjust hand motion based on the force…

cs.HC2023

ACT2G: Attention-based Contrastive Learning for Text-to-Gesture Generation

Hitoshi Teshima, Naoki Wake, Diego Thomas +3

Recent increase of remote-work, online meeting and tele-operation task makes people find that gesture for avatars and communication robots is more important than we have thought. I…

cs.RO20231 cited

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…

cs.RO20231 cited

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…