[Submitted on 5 Oct 2026]
Title:Trajectory Planning without Trajectory Data: A Manifold-Guided Approach
View a PDF of the paper titled Trajectory Planning without Trajectory Data: A Manifold-Guided Approach, by Silong Yong and 7 other authors
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Abstract:A common way for trajectory planning is to leverage generative models trained on large collections of expert trajectories. At inference time, the model generates executable trajectories by conditioning on task goal constraints. However, trajectory-based methods rely on costly supervision, scale poorly with sequence length, and often generalize poorly to unseen constraints such as novel start-goal pairs. We propose an alternative to learn the underlying state-space manifold and use the geometry of the manifold for trajectory planning. This approach requires only state observations and enables generalization to unseen constraints by con- structing trajectories on the learned manifold of the state space. Experiments on Maze2D and robotic motion-planning benchmarks show that Ariadne constructs feasible paths from state-only supervision and generalizes to unseen start-goal combinations. On high-dimensional dual-arm planning, it remains competitive with trajectory-supervised and classical planners, while requiring no trajectory data for training.
Comments: Accepted at NeurIPS 2026. Code can be found in this https URL
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2610.08863 [cs.RO]
(or arXiv:2610.08863v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.08863
arXiv-issued DOI via DataCite
Submission history
From: Silong Yong [view email] [v1] Mon, 5 Oct 2026 20:51:54 UTC (804 KB)
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