将扩散模型的多步生成压缩为单步推理，实现机器人实时控制。
Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models
- 用分因子动态柯尔莫哥洛夫层建模去噪过程，实现单次前向传播。
- 在D4RL和真实机械臂上，推理延迟降至毫秒级，性能优于现有方法。
- 适合需要快速响应的机器人闭环控制场景，兼顾速度与多样性。
扩散模型在机器人规划中能生成多样且多模态的轨迹，但其迭代去噪过程导致延迟过高，难以用于高频闭环控制。为此，本文提出动态神经柯尔莫哥洛夫蒸馏框架，将多步扩散推理压缩为单次前向传播，同时保留教师模型的多模态表达能力。具体地，引入分因子动态柯尔莫哥洛夫层，通过状态依赖的模态增益建模去噪过程中的潜在转移。我们在标准D4RL MuJoCo运动基准和真实Kinova机械臂上进行了评估，对比了一步基线方法。结果表明，该方法在报告的运动任务中显著优于现有一步蒸馏方法，并将推理延迟降低至毫秒级别。硬件实验进一步验证了该方法可实现平滑快速的闭环执行，同时保持任务成功率和相当的精度。项目页面见：https://fdkoopman.github.io/。
原文摘要 · Abstract (English)
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/.
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