Dynamic Neural Koopman Distillation for Fast Robot Control Using Diffusion Models
Dynamic Neural Koopman Distillation (DNK) distills complex diffusion models into a single, fast neural network for robot control. It approximates iterative denoising using state-dependent factorized latent dynamics, achieving millisecond inference speeds suitable for high-frequency closed-loop control while maintaining the multimodal capabilities of diffusion models.