Fast Inference Diffusion Policy for Robot Ball Catching Task

Developed a diffusion-based imitation learning framework with probabilistic dynamic movement primitives that distills multi-step denoising into a single-step inference process, enabling fast action generation and validated on a robotic ball-catching task.