Two-Stage Learning of Highly Dynamic Motions with Rigid and Articulated Soft Quadrupeds

Understand

Controlled execution of dynamic motions in quadrupedal robots, especially those with articulated soft bodies, presents a unique set of challenges that traditional methods struggle to address efficiently.

• In this study, we tackle these issues by relying on a simple yet effective two-stage learning framework to generate dynamic motions for quadrupedal robots.
• First, a gradient-free evolution strategy is employed to discover simply represented control policies, eliminating the need for a predefined reference motion.
• Then, we refine these policies using deep reinforcement learning.