Refining Compositional Diffusion for Reliable Long-Horizon Planning
📰 ArXiv cs.AI
Learn how Refining Compositional Diffusion (RCD) improves long-horizon planning by mitigating mode-averaging issues in compositional diffusion methods
Action Steps
- Implement compositional diffusion planning using score composition to generate long-horizon trajectories
- Identify multimodal local plan distributions that may lead to mode-averaging issues
- Apply Refining Compositional Diffusion (RCD) to mitigate mode-averaging and improve plan feasibility and coherence
- Evaluate the performance of RCD using metrics such as plan success rate and coherence
- Compare the results of RCD with existing compositional diffusion methods to assess its effectiveness
Who Needs to Know This
Researchers and engineers working on planning and decision-making systems can benefit from this technique to generate more reliable long-horizon trajectories
Key Insight
💡 RCD mitigates mode-averaging issues in compositional diffusion planning, leading to more reliable and coherent long-horizon trajectories
Share This
🚀 Improve long-horizon planning with Refining Compositional Diffusion (RCD) and avoid mode-averaging issues! #planning #diffusion
Key Takeaways
Learn how Refining Compositional Diffusion (RCD) improves long-horizon planning by mitigating mode-averaging issues in compositional diffusion methods
Full Article
Title: Refining Compositional Diffusion for Reliable Long-Horizon Planning
Abstract:
arXiv:2605.03075v1 Announce Type: cross Abstract: Compositional diffusion planning generates long-horizon trajectories by stitching together overlapping short-horizon segments through score composition. However, when local plan distributions are multimodal, existing compositional methods suffer from mode-averaging, where averaging incompatible local modes leads to plans that are neither locally feasible nor globally coherent. We propose Refining Compositional Diffusion (RCD), a training-free gui
Abstract:
arXiv:2605.03075v1 Announce Type: cross Abstract: Compositional diffusion planning generates long-horizon trajectories by stitching together overlapping short-horizon segments through score composition. However, when local plan distributions are multimodal, existing compositional methods suffer from mode-averaging, where averaging incompatible local modes leads to plans that are neither locally feasible nor globally coherent. We propose Refining Compositional Diffusion (RCD), a training-free gui
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