Dynamic Legged Ball Manipulation on Rugged Terrains with Hierarchical Reinforcement Learning

Understand

Advancing the dynamic loco-manipulation capabilities of quadruped robots in complex terrains is crucial for performing diverse tasks.

• Specifically, dynamic ball manipulation in rugged environments presents two key challenges.
• The first is coordinating distinct motion modalities to integrate terrain traversal and ball control seamlessly.
• The second is overcoming sparse rewards in end-to-end deep reinforcement learning, which impedes efficient policy convergence.