Riemannian Motion Policy

Riemannian Motion Policies (RMPs) are a reactive motion control framework enabling robots to adapt to dynamic environments and collaborate effectively with humans. Current research focuses on applying RMPs in diverse applications, including industrial human-robot interaction, micro aerial vehicle navigation, and legged locomotion, often integrating them with algorithms like optimal transport for hierarchical policy blending and raycasting for efficient obstacle avoidance. This approach offers advantages in terms of modularity, parallelization, and real-time performance, leading to improved robustness and safety in complex robotic tasks.