Summary: JUNE 2007 IEEE Robotics & Automation Magazine 411070-9932/07/$25.00©2007 IEEE
e propose a model-based reinforcement
learning (RL) algorithm for biped walking
in which the robot learns to appropriately
modulate an observed walking pattern.
Via-points are detected from the observed
walking trajectories using the minimum jerk criterion. The
learning algorithm controls the via-points based on a learned
model of the Poincaré map of the periodic walking pattern.
The model maps from a state in the single support phase and
the controlled via-points to a state in the next single support
phase. We applied this approach to both a simulated robot
model and an actual biped robot. We show that successful
walking policies were acquired.
Sophisticated biped walking controllers have been pro-
posed in robotics , , , . However, human-like
agility, robustness, and energy efficiency have not been
achieved. One possible approach is for the biped controller to