Dynamic Neural Koopman Distillation for Fast Robot Control
Overview
A new framework uses diffusion models to accelerate robot control by distilling multistep inference into a single pass, improving efficiency in locomotion and manipulation tasks.
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- arXiv Robotics — research abstractsDynamic Neural Koopman Distillation for Fast Robot Control Using Diffusion Models
This research proposes a Dynamic Neural Koopman (DNK) distillation framework to accelerate robot control using diffusion models. By distilling multistep diffusion inference into a single forward pass, the method reduces inference latency while maintaining competitive performance on robot control benchmarks. The approach is evaluated on tasks involving locomotion, state-based, and image-based manipulation, showing improved efficiency compared to one-step policy baselines.
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