Improving Sampling-based Motion Control

Abstract

We address several limitations of the sampling-based motion control method of Liu et at. [LYvdP 10]. The key insight is to learn from the past control reconstruction trials through sample distribution adaptation. Coupled with a sliding window scheme for better performance and an averaging method for noise reduction, the improved algorithm can efficiently construct open-loop controls for long and challenging reference motions in good quality. Our ideas are intuitive and the implementations are simple. We compare the improved algorithm with the original algorithm both qualitatively and quantitatively, and demonstrate the effectiveness of the improved algorithm with a variety of motions ranging from stylized walking and dancing to gymnastic and Martial Arts routines.