mlsanityData to 14 Oct 2026

Diffusion-Based Multiple-Shooting Indirect Optimal Control for Fuel-Optimal Spacecraft Trajectory Generation

arXiv 2609.13990 ↗First posted 12 Sep 2026eess.SY cs.LG cs.SYcited by 0

On the map: Diffusion models → Diffusion and flow policies for robotics

Abstract (arXiv)

Diffusion-based generative models (DMs) have found applications in control problems, and in particular robotics, where the DMs enable exploration of possible control solutions. A critical shortcoming of these applications is that they have lacked optimality guarantees. This is a problem for their potential use in fuel-optimal spacecraft trajectories that are characterized with long time-horizons and bang-bang profiles. Alternatively, indirect optimal control methods ensure explicit satisfaction of necessary conditions, but are highly sensitive to the initial costate estimation needed to solve the resulting Hamiltonian boundary-value problems (HBVPs). To alleviate this sensitivity and enlarge the convergence domain of HBVPs, advanced indirect methods have been developed that use smoothing approaches and continuation. We propose a diffusion-based multiple shooting indirect control method that combines the exploration capability of DMs with indirect method to generate fuel-optimal spacecraft trajectories. We benchmark our method against an advanced indirect method on a fuel-optimal Earth-Mars low-thrust transfer problem, showing higher convergence robustness than the advanced indirect method that is based on random costate initialization. Code and visualizations are available at https://saeidtafazzol.github.io/Diffusion_Indirect_Control/.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 12 of its 27 references that are in the map. First posted on arXiv 12 Sep 2026.

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  1. 2025Gradient-Informed Monte Carlo Fine-Tuning of Diffusion Models for Low-Thrust Trajectory Design9 Dec
  2. 2024Policy-Guided Diffusion9 Apr
  3. 2023Diffusion Policy: Visuomotor Policy Learning via Action Diffusion7 Mar

Other references · 9

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  4. 2022Classifier-Free Diffusion Guidancecited by 5,847
  5. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,061
  6. 2022On Neural Differential Equationscited by 268
  7. 2025Global Search for Optimal Low Thrust Spacecraft Trajectories using Diffusion Models and the Indirect Methodcited by 8
  8. 2024Comparison of control regularization techniques for minimum-fuel low-thrust trajectory design using indirect methodscited by 1
  9. 2025Co-state Neural Network for Real-time Nonlinear Optimal Control with Input Constraintscited by 1

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  4. 2026Transfer Learning of Multiobjective Indirect Low-Thrust Trajectories Using Diffusion Models and Markov Chain Monte Carlo
  5. 2025Self-supervised diffusion model fine-tuning for costate initialization using Markov chain Monte Carlo
  6. 2024Equality Constrained Diffusion for Direct Trajectory Optimization
  7. 2025PAD-TRO: Projection-Augmented Diffusion for Direct Trajectory Optimization
  8. 2026Spacecraft Rendezvous Trajectory Generation with Modular Constraints via Diffusion Model Composition