# Agile Tradespace Exploration for Space Rendezvous Mission Design via Transformers arXiv: https://arxiv.org/abs/2510.03544 ยท HTML: https://mlsanity.com/paper/2510.03544/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 70 references that are in the map. - First posted on arXiv 3 Oct 2025. ## Builds on (most cited first, 24 of 24) - [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021) - [Scaling Pareto-Efficient Decision Making Via Offline Multi-Objective RL](https://mlsanity.com/paper/2305.00567/) (2023) - [Transformers for Trajectory Optimization with Application to Spacecraft Rendezvous](https://mlsanity.com/paper/2310.13831/) (2023) - [Transformer-based Model Predictive Control: Trajectory Optimization via Sequence Modeling](https://mlsanity.com/paper/2410.23916/) (2024) - [Amortized Global Search for Efficient Preliminary Trajectory Design with Deep Generative Models](https://mlsanity.com/paper/2308.03960/) (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) - [GuSTO: Guaranteed Sequential Trajectory Optimization via Sequential Convex Programming](https://mlsanity.com/paper/1903.00155/) (2019) - [Global Search for Optimal Low Thrust Spacecraft Trajectories using Diffusion Models and the Indirect Method](https://mlsanity.com/paper/2501.07005/) (2025) - [Successive Convexification with Feasibility Guarantee via Augmented Lagrangian for Non-Convex Optimal Control Problems](https://mlsanity.com/paper/2304.14564/) (2023) - [Constraint-Informed Learning for Warm Starting Trajectory Optimization](https://mlsanity.com/paper/2312.14336/) (2023) - [Generalizable Spacecraft Trajectory Generation via Multimodal Learning with Transformers](https://mlsanity.com/paper/2410.11723/) (2024) - [Passively-Safe and Robust Multi-Agent Optimal Control with Application to Distributed Space Systems](https://mlsanity.com/paper/2209.02096/) (2022) - [Autonomous Guidance Navigation and Control of the VISORS Formation-Flying Mission](https://mlsanity.com/paper/2309.16698/) (2023) - [Fast Homotopy for Spacecraft Rendezvous Trajectory Optimization with Discrete Logic](https://mlsanity.com/paper/2107.07001/) (2021) - [Deep Learning Warm Starts for Trajectory Optimization on the International Space Station](https://mlsanity.com/paper/2505.05588/) (2025) - [Chance-Constrained, Drift-Safe Guidance for Spacecraft Rendezvous](https://mlsanity.com/paper/2401.11077/) (2024) - [Safe and Optimal N-Spacecraft Swarm Reconfiguration in Non-Keplerian Cislunar Orbits](https://mlsanity.com/paper/2504.20386/) (2025) - [Neural-Rendezvous: Provably Robust Guidance and Control to Encounter Interstellar Objects](https://mlsanity.com/paper/2208.04883/) (2022) - [Filtering-Linearization: A First-Order Method for Nonconvex Trajectory Optimization with Filter-Based Warm-Starting](https://mlsanity.com/paper/2409.17944/) (2024) - [Compositional Diffusion Models for Powered Descent Trajectory Generation with Flexible Constraints](https://mlsanity.com/paper/2410.04261/) (2024) - [Robust Constrained Multi-objective Evolutionary Algorithm based on Polynomial Chaos Expansion for Trajectory Optimization](https://mlsanity.com/paper/2205.11387/) (2022) - [Automated Tour Design in the Saturnian System](https://mlsanity.com/paper/2210.14996/) (2022) - [Initial Guess Generation for Low-Thrust Trajectory Design with Robustness to Missed-Thrust-Events](https://mlsanity.com/paper/2501.06694/) (2025) ## Built on by (most cited first, 4 of 4) - [Language-Conditioned Safe Trajectory Generation for Spacecraft Rendezvous](https://mlsanity.com/paper/2512.09111/) (2025) - [Intent-aligned Autonomous Spacecraft Guidance via Reasoning Models](https://mlsanity.com/paper/2604.17176/) (2026) - [Transformer-Based Warm-Starting for Feasible and Optimal Terminal Approach to Tumbling Objects with Space Manipulators](https://mlsanity.com/paper/2606.17317/) (2026) - [Transformer-Informed Trajectory Optimization for Relative Motion in Cislunar Orbits](https://mlsanity.com/paper/2609.25460/) (2026) ## Abstract (arXiv) Spacecraft rendezvous enables on-orbit servicing, debris removal, and crewed docking, forming the foundation for a scalable space economy. Designing such missions requires rapid exploration of the tradespace between control cost and flight time across multiple candidate targets. However, multi-objective optimization in this setting is challenging, as the underlying constraints are often nonconvex, and mission designers must balance accuracy (e.g., solving the full problem) with efficiency (e.g., convex relaxations), slowing iteration and limiting design agility. To address these challenges, this paper proposes an AI-powered framework that enables agile and generalized rendezvous mission design. Given the orbital information of the target spacecraft, boundary conditions of the servicer, and a range of flight times, a transformer model generates a set of near-Pareto optimal trajectories across varying flight times in a single parallelized inference step, thereby enabling rapid mission trade studies. The model is further extended to accommodate variable flight times and perturbed orbital dynamics, supporting realistic multi-objective trade-offs. Validation on chance-constrained rendezvous problems in Earth orbits with passive safety constraints demonstrates that the model generalizes across both flight times and dynamics, consistently providing high-quality initial guesses that converge to superior solutions in fewer iterations. Moreover, the framework efficiently approximates the Pareto front, achieving runtimes comparable to convex relaxation by exploiting parallelized inference. Together, these results position the proposed framework as a practical surrogate for nonconvex trajectory generation and mark an important step toward AI-driven trajectory design for accelerating preliminary mission planning in real-world rendezvous applications.