Kuniyuki Takahashi

Preferred Networks (Japan), Waseda University, Japan Society for the Promotion of Science, Chuo University

论文数

23

总引用数

470

H-Index

9

关于

暂无简介。

研究焦点

Artificial intelligence23 · 470 次引用
Computer science23 · 470 次引用
Robot21 · 466 次引用
Engineering11 · 311 次引用
Task (project management)5 · 240 次引用
Human–computer interaction4 · 238 次引用
Object (grammar)7 · 237 次引用
Computer vision14 · 208 次引用
Comprehension2 · 188 次引用
Natural language processing2 · 188 次引用
Ambiguity2 · 188 次引用
Natural language2 · 188 次引用

主要成就

9
H 指数
23
论文
470
总引用数
20
篇均引用
🏆 最高被引论文
Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions
175 次引用 · 2018
📈 最高产年份: 2018 (4 论文)
🤝 主要合作者: 47
🏛 所属机构: Preferred Networks (Japan), Waseda University, Japan Society for the Promotion of Science, Chuo University

代表论文

  1. 1
    Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions
    175 次引用 · 2018
  2. 2
    Deep Visuo-Tactile Learning: Estimation of Tactile Properties from Images
    70 次引用 · 2019
  3. 3
    Tool-body assimilation model considering grasping motion through deep learning
    41 次引用 · 2017
  4. 4
    Deep Gated Multi-modal Learning: In-hand Object Pose Changes Estimation using Tactile and Image Data
    33 次引用 · 2020
  5. 5
    Uncertainty-aware Self-supervised Target-mass Grasping of Granular Foods
    26 次引用 · 2021
  6. 6
    Dynamic motion learning for multi-DOF flexible-joint robots using active–passive motor babbling through deep learning
    25 次引用 · 2017
  7. 7
    Neural network based model for visual-motor integration learning of robot's drawing behavior: Association of a drawing motion from a drawn image
    13 次引用 · 2015
  8. 8
    Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions
    13 次引用 · 2017
  9. 9
    Tool-Body Assimilation Model Based on Body Babbling and Neurodynamical System
    12 次引用 · 2015
  10. 10
    Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning
    9 次引用 · 2017

主要合作者

TOTetsuya Ogata10 篇合著论文SSShigeki Sugano9 篇合著论文JTJethro Tan5 篇合著论文WKWilson Ko5 篇合著论文HTHadi Tjandra5 篇合著论文AUAvinash Ummadisingu3 篇合著论文SMShin‐ichi Maeda3 篇合著论文YTYuta Tsuboi2 篇合著论文JHJun Hatori2 篇合著论文SMShimpei Masuda2 篇合著论文SKSosuke Kobayashi2 篇合著论文YKYuta Kikuchi2 篇合著论文YUYuya Unno2 篇合著论文SMShingo Murata2 篇合著论文TATomoki Anzai2 篇合著论文HIHiroyasu Iwata1 篇合著论文KSKanata Suzuki1 篇合著论文SHShohei Hama1 篇合著论文KNKuniaki Noda1 篇合著论文RHRyo Hirai1 篇合著论文
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