Motion Primitive

Motion primitives are compact representations of movement segments used in robotics and animation to generate complex, dynamic behaviors. Current research focuses on developing efficient and robust methods for learning, generating, and composing these primitives, often employing techniques like diffusion models, reinforcement learning, and probabilistic approaches such as ProDMPs and normalizing flows, to achieve real-time control and adaptability in diverse environments. This work is significant for advancing autonomous navigation, dexterous manipulation, and human-robot interaction, particularly in applications requiring adaptability to uncertainty and complex constraints. The resulting improvements in planning efficiency and control accuracy have broad implications across various robotic domains.