arXiv (All)
open access
Dynamic Neural Koopman Distillation for Fast Robot Control Using Diffusion Models
Diffusion models excel at generating diverse, multimodal trajectories for robotic control, yet their iterative denoising process introduces latency that can constrain real-time closed-loop execution. To address this problem, we propose a Dynamic Neural Koopman (DNK) distillation framework, which dis
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Learning Dynamic Stability Landscapes in Synchronization Networks#222594
- Continuous Spiking Graph Neural Networks#1035261
- SNAPO: Smooth Neural Adjoint Policy Optimization for Optimal Control via Differentiable Simulation#168331
- ManiFusion: Unlocking the High-Throughput Generation via Superposition in Manifold Space#1356769
- Global Dynamics and Synchronization of Hodgkin-Huxley-Wilson Neural Networks#1232728
Record · ID 1233861
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive