# Spacecraft Rendezvous Trajectory Generation with Modular Constraints via Diffusion Model Composition

arXiv: https://arxiv.org/abs/2610.05642 · HTML: https://mlsanity.com/paper/2610.05642/

## 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 24 of its 31 references that are in the map.
- First posted on arXiv 5 Oct 2026.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Builds on (most cited first, 24 of 24)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)
- [Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC](https://mlsanity.com/paper/2302.11552/) (2023)
- [Compositional Visual Generation and Inference with Energy Based Models](https://mlsanity.com/paper/2004.06030/) (2020)
- [DiffuSolve: Diffusion-based Solver for Non-convex Trajectory Optimization](https://mlsanity.com/paper/2403.05571/) (2024)
- [Transformers for Trajectory Optimization with Application to Spacecraft Rendezvous](https://mlsanity.com/paper/2310.13831/) (2023)
- [Successive Convexification of Non-Convex Optimal Control Problems and Its Convergence Properties](https://mlsanity.com/paper/1608.05133/) (2016)
- [Towards Robust Spacecraft Trajectory Optimization via Transformers](https://mlsanity.com/paper/2410.05585/) (2024)
- [Planning as In-Painting: A Diffusion-Based Embodied Task Planning Framework for Environments under Uncertainty](https://mlsanity.com/paper/2312.01097/) (2023)
- [Dynamical Diffusion: Learning Temporal Dynamics with Diffusion Models](https://mlsanity.com/paper/2503.00951/) (2025)
- [Global Search for Optimal Low Thrust Spacecraft Trajectories using Diffusion Models and the Indirect Method](https://mlsanity.com/paper/2501.07005/) (2025)
- [Aligning Diffusion Model with Problem Constraints for Trajectory Optimization](https://mlsanity.com/paper/2504.00342/) (2025)
- [Diffusion Policies for Generative Modeling of Spacecraft Trajectories](https://mlsanity.com/paper/2501.00915/) (2025)
- [Fast Trajectory Optimization via Successive Convexification for Spacecraft Rendezvous with Integer Constraints](https://mlsanity.com/paper/1906.04857/) (2019)
- [Guided Policy Search using Sequential Convex Programming for Initialization of Trajectory Optimization Algorithms](https://mlsanity.com/paper/2110.06975/) (2021)
- [Improving Computational Efficiency for Powered Descent Guidance via Transformer-based Tight Constraint Prediction](https://mlsanity.com/paper/2311.05135/) (2023)
- [Diffusion Models for Generating Ballistic Spacecraft Trajectories](https://mlsanity.com/paper/2405.11738/) (2024)
- [Multi-Phase Spacecraft Trajectory Optimization via Transformer-Based Reinforcement Learning](https://mlsanity.com/paper/2511.11402/) (2025)
- [Compositional Diffusion Models for Powered Descent Trajectory Generation with Flexible Constraints](https://mlsanity.com/paper/2410.04261/) (2024)
- [Imitation learning-based spacecraft rendezvous and docking method with Expert Demonstration](https://mlsanity.com/paper/2601.12952/) (2026)

## Abstract (arXiv)

Emerging mission classes such as on-orbit servicing, satellite inspection, and active debris removal require trajectory design methods that are adaptable to a variety of mission scenarios. We present a diffusion-based trajectory generation approach for rendezvous and proximity operations (RPO) that enables flexible configuration of mission constraints. First, individual energy-based diffusion models are trained to satisfy distinct constraints such as approach cone and sensor line-of-sight from a set of optimized trajectories. Then, at inference time, the learned energy models can be composed with one another, or with an analytically defined energy field, to enforce specific constraint combinations. We validate this framework with the composition of a learned approach cone model and a learned sensor line-of-sight model, as well as a learned approach cone model and synthetic obstacle avoidance model, both of which yield constraint satisfaction rates that are within 1 percentage point of the single-constraint models or higher. These results indicate that our compositional diffusion framework can provide a modular approach to RPO trajectory design and enable reconfiguration for new constraint combinations without requiring model retraining.