Accelerating trajectory optimization with Sobolev-trained diffusion policies
Trajectory Optimization (TO) solvers exploit known system dynamics to compute locally optimal trajectories through iterative improvements. A downside is that each new problem instance is solved independently; therefore, convergence speed and quality of the solution found depend on the initial trajectory proposed. To improve efficiency, a natural approach is to warm-start TO with initial guesses produced by a learned policy trained on trajectories previously generated by the solver. Diffusion-based policies have recently emerged as expressive imitation learning models, making them promising candidates for this role. Yet, a counterintuitive challenge comes from the local optimality of TO de...
| Authors | Théotime Le Hellard, Franki Nguimatsia Tiofack, Quentin Le Lidec et al. |
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| Primary category | cs.LG |
| Published | 21 April 2026 |
| Last updated at source | 28 May 2026 |
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