Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.20102 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917082288881664 |
|---|---|
| author | Chen, Chang Hamed, Hany Baek, Doojin Kang, Taegu Noh, Samyeul Bengio, Yoshua Ahn, Sungjin |
| author_facet | Chen, Chang Hamed, Hany Baek, Doojin Kang, Taegu Noh, Samyeul Bengio, Yoshua Ahn, Sungjin |
| contents | Long-horizon planning is crucial in complex environments, but diffusion-based planners like Diffuser are limited by the trajectory lengths observed during training. This creates a dilemma: long trajectories are needed for effective planning, yet they degrade model performance. In this paper, we introduce this extendable long-horizon planning challenge and propose a two-phase solution. First, Progressive Trajectory Extension incrementally constructs longer trajectories through multi-round compositional stitching. Second, the Hierarchical Multiscale Diffuser enables efficient training and inference over long horizons by reasoning across temporal scales. To avoid the need for multiple separate models, we propose Adaptive Plan Pondering and the Recursive HM-Diffuser, which unify hierarchical planning within a single model. Experiments show our approach yields strong performance gains, advancing scalable and efficient decision-making over long-horizons. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20102 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extendable Planning via Multiscale Diffusion Chen, Chang Hamed, Hany Baek, Doojin Kang, Taegu Noh, Samyeul Bengio, Yoshua Ahn, Sungjin Machine Learning Robotics Long-horizon planning is crucial in complex environments, but diffusion-based planners like Diffuser are limited by the trajectory lengths observed during training. This creates a dilemma: long trajectories are needed for effective planning, yet they degrade model performance. In this paper, we introduce this extendable long-horizon planning challenge and propose a two-phase solution. First, Progressive Trajectory Extension incrementally constructs longer trajectories through multi-round compositional stitching. Second, the Hierarchical Multiscale Diffuser enables efficient training and inference over long horizons by reasoning across temporal scales. To avoid the need for multiple separate models, we propose Adaptive Plan Pondering and the Recursive HM-Diffuser, which unify hierarchical planning within a single model. Experiments show our approach yields strong performance gains, advancing scalable and efficient decision-making over long-horizons. |
| title | Extendable Planning via Multiscale Diffusion |
| topic | Machine Learning Robotics |
| url | https://arxiv.org/abs/2503.20102 |