Trajectory Planning without Trajectory Data: A Manifold-Guided Approach

What happened

arXiv:2610.08863v1 Announce Type: new Abstract: A common way for trajectory planning is to leverage generative models trained on large collections of expert trajectories. At inference (running a trained model to get an answer, rather than training it) 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. 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.