Foundation Models for Humanoid Motion Learning
2024年1月1日
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1 min read
We apply foundation models such as GPT to humanoid motor control, building unified policies that handle diverse motion tasks including locomotion and whole-body actions. By leveraging the general-purpose reasoning of pretrained models and refining them with action data, we aim at data-efficient, multi-task humanoid control.
Related papers:
- Siddharth Padmanabhan, Kazuki Miyazawa, Takato Horii. “LocoGPT: GPT-Based Multi-Humanoid-Task Policy for Humanoid Locomotion”. IEEE Access, Vol. 14, 2026.
- Siddharth Padmanabhan, Kazuki Miyazawa, Takato Horii, Takayuki Nagai. “Data-Efficient Approach to Humanoid Control by Fine-Tuning a Pre-Trained GPT on Action Data”. IEEE Access, Vol. 13, 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.