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

• URL: http://arxiv.org/abs/2605.24924v1
• Date: Sun, 24 May 2026 08:03:49 GMT
• Title: Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models
• Abstract summary: We propose Dynamic Neural Koopman Distillation, a framework that distills multistep diffusion inference into a single forward pass.<n>We evaluate the proposed method on standard D4RL MuJoCo locomotion benchmarks and a physical Kinova manipulator.
• Score: 11.615273931053915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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/.

Related papers

• Ada3Drift: Adaptive Training-Time Drifting for One-Step 3D Visuomotor Robotic Manipulation [53.750389076941396]
  Diffusion-based visuomotor policies capture multimodal action distributions through iterative denoising, but their high inference latency limits real-time robotic control.<n>We propose Ada3Drift, which learns a training-time drifting field that attracts predicted actions toward expert demonstration modes.<n>Ada3Drift achieves state-of-the-art performance while requiring $10times$ fewer function evaluations than diffusion-based alternatives.
  arXiv  Detail & Related papers  (2026-03-12T14:36:53Z)
• DyWeight: Dynamic Gradient Weighting for Few-Step Diffusion Sampling [18.524470249911232]
  Dynamic Gradient Weighting (DyWeight) is a lightweight, learning-based multi-step solver.<n>DyWeight learns unconstrained time-varying parameters that adaptively aggregate historical gradients.<n>Experiments on CIFAR-10, FFHQ, AFHQv2, ImageNet64, LSUN-Bedroom, Stable Diffusion and FLUX.1-dev demonstrate that DyWeight achieves superior visual fidelity and stability.
  arXiv  Detail & Related papers  (2026-03-12T07:01:44Z)
• From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation [18.70033095161235]
  We propose a framework that distills a Conditional Flow Matching expert into a fast single-step student via Implicit Likelihood Estimation (IMLE)<n>A bi-directional Chamfer distance provides a set-level objective that promotes both mode coverage and fidelity.<n>A unified perception encoder further integrates multi-view RGB, depth, point clouds, and proprioception into a geometry-aware representation.
  arXiv  Detail & Related papers  (2026-03-10T09:30:05Z)
• One-Step Diffusion Model for Image Motion-Deblurring [85.76149042561507]
  We propose a one-step diffusion model for deblurring (OSDD), a novel framework that reduces the denoising process to a single step.<n>To tackle fidelity loss in diffusion models, we introduce an enhanced variational autoencoder (eVAE), which improves structural restoration.<n>Our method achieves strong performance on both full and no-reference metrics.
  arXiv  Detail & Related papers  (2025-03-09T09:39:57Z)
• LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformers [79.07412045476872]
  Diffusion Transformers have emerged as the preeminent models for a wide array of generative tasks.<n>We show that performing the full of the model at each diffusion step is unnecessary, as some computations can be skipped by lazily reusing the results of previous steps.<n>We propose a lazy learning framework that efficiently leverages cached results from earlier steps to skip redundant computations.
  arXiv  Detail & Related papers  (2024-12-17T01:12:35Z)
• One-Step Diffusion Policy: Fast Visuomotor Policies via Diffusion Distillation [80.71541671907426]
  OneStep Diffusion Policy (OneDP) is a novel approach that distills knowledge from pre-trained diffusion policies into a single-step action generator. OneDP significantly accelerates response times for robotic control tasks.
  arXiv  Detail & Related papers  (2024-10-28T17:54:31Z)
• DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions [52.63323657077447]
  We propose DNMOT, an end-to-end trainable DeNoising Transformer for multiple object tracking. Specifically, we augment the trajectory with noises during training and make our model learn the denoising process in an encoder-decoder architecture. We conduct extensive experiments on the MOT17, MOT20, and DanceTrack datasets, and the experimental results show that our method outperforms previous state-of-the-art methods by a clear margin.
  arXiv  Detail & Related papers  (2023-09-09T04:40:01Z)
• Value function estimation using conditional diffusion models for control [62.27184818047923]
  We propose a simple algorithm called Diffused Value Function (DVF) It learns a joint multi-step model of the environment-robot interaction dynamics using a diffusion model. We show how DVF can be used to efficiently capture the state visitation measure for multiple controllers.
  arXiv  Detail & Related papers  (2023-06-09T18:40:55Z)
• Learning Unstable Dynamics with One Minute of Data: A Differentiation-based Gaussian Process Approach [47.045588297201434]
  We show how to exploit the differentiability of Gaussian processes to create a state-dependent linearized approximation of the true continuous dynamics. We validate our approach by iteratively learning the system dynamics of an unstable system such as a 9-D segway.
  arXiv  Detail & Related papers  (2021-03-08T05:08:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.