Extendable Long-Horizon Planning via Hierarchical Multiscale Diffusion

• URL: http://arxiv.org/abs/2503.20102v2
• Date: Thu, 10 Apr 2025 19:53:26 GMT
• Title: Extendable Long-Horizon Planning via Hierarchical Multiscale Diffusion
• Authors: Chang Chen, Hany Hamed, Doojin Baek, Taegu Kang, Yoshua Bengio, Sungjin Ahn,
• Abstract summary: This paper tackles a novel problem, extendable long-horizon planning-enabling agents to plan trajectories longer than those in training data without compounding errors.<n>We propose an augmentation method that iteratively generates longer trajectories by stitching shorter ones.<n>HM-Diffuser trains on these extended trajectories using a hierarchical structure, efficiently handling tasks across multiple temporal scales.
• Score: 62.91968752955649
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper tackles a novel problem, extendable long-horizon planning-enabling agents to plan trajectories longer than those in training data without compounding errors. To tackle this, we propose the Hierarchical Multiscale Diffuser (HM-Diffuser) and Progressive Trajectory Extension (PTE), an augmentation method that iteratively generates longer trajectories by stitching shorter ones. HM-Diffuser trains on these extended trajectories using a hierarchical structure, efficiently handling tasks across multiple temporal scales. Additionally, we introduce Adaptive Plan Pondering and the Recursive HM-Diffuser, which consolidate hierarchical layers into a single model to process temporal scales recursively. Experimental results demonstrate the effectiveness of our approach, advancing diffusion-based planners for scalable long-horizon planning.

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