[Paper] Distribution-Agnostic Robust Trajectory Optimization via Chance-Constrained Reinforcement Learning
Source: arXiv - 2606.13605v1
Overview
This paper presents a distribution-agnostic robust trajectory-optimization framework based on chance-constrained reinforcement learning. The uncertainty is represented here through initial conditions and process noise, with the only requirement being that it can be sampled. A deterministic nominal trajectory is first computed offline, and reinforcement learning is then used only to robustify that baseline through a structured affine closed-loop correction law comprising a feedforward control adjustment and time-varying feedback gains. Probabilistic feasibility is enforced empirically through rollout-based upper-tail quantiles, while terminal dispersion is regulated through covariance-feasibility penalties. The framework is assessed on two materially different trajectory design problems. The flagship case study is a three-dimensional multi-impulse Earth-Mars transfer, where the learned policy is benchmarked against a recent robust trajectory-optimization reference under Gaussian uncertainty and then evaluated under bounded uniform uncertainty and under process disturbances not seen during training. The second case study is a stochastic atmospheric pinpoint rocket landing problem, used to assess portability to a short-horizon continuous-thrust setting with drag, mass depletion, and glide-slope constraints. The results show that the proposed framework can remain competitive in upper-tail fuel cost while preserving probabilistic feasibility, and that the same robustification scaffold can be carried across heterogeneous spacecraft trajectory planning problems without redesign of its core stochastic-control structure.
Key Contributions
This paper presents research in the following areas:
- math.OC
- cs.LG
- eess.SY
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of math.OC.
Authors
- Yashdeep Chaudhary
- Roberto Armellin
- Harry Holt
- Marco Sagliano
Paper Information
- arXiv ID: 2606.13605v1
- Categories: math.OC, cs.LG, eess.SY
- Published: June 11, 2026
- PDF: Download PDF