Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models

2026-05-24

AI Summary

While diffusion models offer powerful multimodal trajectory generation for robotic motion planning, their iterative denoising process incurs high latency, hindering deployment in high-frequency closed-loop control. To address this limitation, this work proposes a Dynamic Neural Koopman Distillation framework that introduces a factorized dynamic Koopman layer to compress multi-step diffusion inference into a single forward pass. This approach preserves the teacher model’s multimodal expressiveness while achieving millisecond-level inference latency. By synergistically integrating diffusion models, knowledge distillation, and Koopman operator theory, the method substantially outperforms existing single-step distillation approaches on the D4RL MuJoCo benchmark and enables smooth, efficient real-time closed-loop control on a Kinova robotic arm.

Abstract

Diffusion models excel at generating diverse and multimodal trajectories for robotic planning, yet their iterative denoising process introduces latency that is incompatible with high-frequency closed-loop control. To address this problem, we propose Dynamic Neural Koopman Distillation, a framework that distills multistep diffusion inference into a single forward pass while retaining the multimodal expressivity of the teacher model. Specifically, we introduce a Factorized Dynamic Koopman layer that models the denoising process through a factorized latent transition with state-dependent modal gains. We evaluate the proposed method on standard D4RL MuJoCo locomotion benchmarks and a physical Kinova manipulator, comparing against one-step baselines. The results show that our method significantly outperforms existing one-step distillation approaches on the reported locomotion tasks, and reduces the inference latency to the millisecond regime compared with the teacher policy. Hardware experiments further demonstrate that our method enables smooth and fast closed-loop execution while maintaining task success and comparable accuracy. A project page is available at https://fdkoopman.github.io/.

Problem

• diffusion models
• real-time control
• robotic planning
• inference latency
• closed-loop control

Innovation

• Diffusion Models
• Koopman Operator
• Policy Distillation
• Real-Time Control
• Multimodal Trajectory Generation