Hybrid Diffusion for Simultaneous Symbolic and Continuous Planning
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910175119540224 |
|---|---|
| author | Høeg, Sigmund Hennum Vaaler, Aksel Liu, Chaoqi Egeland, Olav Du, Yilun |
| author_facet | Høeg, Sigmund Hennum Vaaler, Aksel Liu, Chaoqi Egeland, Olav Du, Yilun |
| contents | Constructing robots to accomplish long-horizon tasks is a long-standing challenge within artificial intelligence. Approaches using generative methods, particularly Diffusion Models, have gained attention due to their ability to model continuous robotic trajectories for planning and control. However, we show that these models struggle with long-horizon tasks that involve complex decision-making and, in general, are prone to confusing different modes of behavior, leading to failure. To remedy this, we propose to augment continuous trajectory generation by simultaneously generating a high-level symbolic plan. We show that this requires a novel mix of discrete variable diffusion and continuous diffusion, which dramatically outperforms the baselines. In addition, we illustrate how this hybrid diffusion process enables flexible trajectory synthesis, allowing us to condition synthesized actions on partial and complete symbolic conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21983 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid Diffusion for Simultaneous Symbolic and Continuous Planning Høeg, Sigmund Hennum Vaaler, Aksel Liu, Chaoqi Egeland, Olav Du, Yilun Robotics Artificial Intelligence Constructing robots to accomplish long-horizon tasks is a long-standing challenge within artificial intelligence. Approaches using generative methods, particularly Diffusion Models, have gained attention due to their ability to model continuous robotic trajectories for planning and control. However, we show that these models struggle with long-horizon tasks that involve complex decision-making and, in general, are prone to confusing different modes of behavior, leading to failure. To remedy this, we propose to augment continuous trajectory generation by simultaneously generating a high-level symbolic plan. We show that this requires a novel mix of discrete variable diffusion and continuous diffusion, which dramatically outperforms the baselines. In addition, we illustrate how this hybrid diffusion process enables flexible trajectory synthesis, allowing us to condition synthesized actions on partial and complete symbolic conditions. |
| title | Hybrid Diffusion for Simultaneous Symbolic and Continuous Planning |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2509.21983 |