Learning Humanoid Locomotion
This research applies our reinforcement learning methods to learning locomotion by a human-size humanoid robot. A new learning scheme is studied where the robot is embedded with a primitive balancing controller during learning.
In this research, we collaborate with ATR Computational Neuroscience Laboratories and Prof. Sang-Ho Hyon.
Related Papers
- Akihiko Yamaguchi, Sang-Ho Hyon, and Tsukasa Ogasawara:
Reinforcement Learning for Balancer Embedded Humanoid Locomotion,
in Proceedings of the 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids2010), pp.308-313, Nashville, TN, US, 2010. [final-draft]