Autonomous Motion Learning via Large Language Models
2024年1月1日
·
1 min read
Using large language models (LLMs) and vision-language models (VLMs), the robot autonomously generates demonstrations from task instructions, enabling motion-skill learning without expert teaching or pre-defined motion primitives. Through an affordance-centric diffusion policy, we integrate the precise control of imitation learning with the generalization of foundation models.
Related papers:
- Hu Site, Takayuki Nagai, Takato Horii. “TARAD: Task-Aware Robot Affordance-Centric Diffusion Policy Learned From LLM-Generated Demonstrations”. IEEE Robotics and Automation Letters, August 2025.
Authors
Takato Horii
(he/him)
Associate Professor
Associate Professor at Graduate School of Engineering Science, Osaka University.
Research interests include cognitive developmental robotics, computational modeling
of emotional development, and human-robot interaction.