Abstract: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.
| Comments: | 10 pages, 11 figures. This work has been submitted to the IEEE for possible publication. See this https URL for the project website |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2509.21983 [cs.RO] |
| (or arXiv:2509.21983v2 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2509.21983 arXiv-issued DOI via DataCite |
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| Journal reference: | IEEE Robotics and Automation Letters, vol. 11, no. 4, pp. 4489-4496, April 2026 |
| Related DOI: | https://doi.org/10.1109/LRA.2026.3664616
DOI(s) linking to related resources |
Submission history
From: Sigmund Hennum Høeg [view email]
[v1]
Fri, 26 Sep 2025 07:06:26 UTC (41,267 KB)
[v2]
Tue, 28 Apr 2026 19:33:40 UTC (41,441 KB)