Tatsuya Aoki

About

Tatsuya Aoki is a leading researcher at the intersection of cognitive robotics, language acquisition, and multi-agent planning. His work focuses on bridging the gap between low-level motor control and high-level symbolic reasoning, aiming to create robots that can learn, plan, and communicate as naturally as humans. Aoki’s most influential contribution is his proposed integrated cognitive architecture for robot learning of action and language (2019, 24 citations), which models how robots can simultaneously acquire motor skills and linguistic understanding. He has also pioneered the use of Large Language Models in robotics with LiP-LLM (2024, 14 citations), a framework that combines linear programming and dependency graphs for efficient multi-robot task planning. Earlier foundational work includes motor babbling for learning motor control (2016, 9 citations) and an online multimodal learning algorithm for object concepts and language (2016, 9 citations). Supported by CREST, JST, Aoki’s research is shaping the future of embodied intelligence, where robots autonomously develop cognitive and linguistic capabilities through real-world interaction.

Research Focus

Key Achievements

Top Papers

1. 1
   Integrated Cognitive Architecture for Robot Learning of Action and Language

   24 citations · 2019
2. 2
   LiP-LLM: Integrating Linear Programming and Dependency Graph With Large Language Models for Multi-Robot Task Planning

   14 citations · 2024
3. 3
   Learning of Motor Control from Motor Babbling**This research is supported by CREST, JST.

   9 citations · 2016
4. 4
   Online joint learning of object concepts and language model using multimodal hierarchical Dirichlet process

   9 citations · 2016

Key Collaborators

Contact & Links