Learning and Exploring Motor Skills with Spacetime Bounds

Understand

Equipping characters with diverse motor skills is the current bottleneck of physics-based character animation.

• We propose a Deep Reinforcement Learning (DRL) framework that enables physics-based characters to learn and explore motor skills from reference motions.
• The key insight is to use loose space-time constraints, termed spacetime bounds, to limit the search space in an early termination fashion.
• As we only rely on the reference to specify loose spacetime bounds, our learning is more robust with respect to low quality references.