The paper introduces Reachability Analysis-Informed Reinforcement Learning (RARL) for designing deterministic multi‑impulse interplanetary transfers. RARL uses local first‑order reachability maps to bound velocity perturbations and selects intermediate waypoints, which are then translated into maneuvers via Lambert reconstruction and a terminal two‑impulse solution. Numerical experiments on an Earth‑Mars benchmark show that RARL achieves a mean maneuver cost only 1.72% above a validated convex programming reference and can be trained once to handle a wide range of departure states, achieving 100% feasibility on 10,000 held‑out Monte Carlo departures.
By Yashdeep Chaudhary, Roberto Armellin, Harry Holt
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.
The paper introduces Action Diffusion, a conditional diffusion model that generates action sequences for a point-mass system affected by dry friction and stiction. Using a compact 1D U‑Net, the model produces bounded control sequences conditioned on initial and target states, outperforming uniform random shooting, structured random shooting, and the Cross‑Entropy Method in reducing terminal error and stuck steps, especially with few samples. The results demonstrate that conditional diffusion can produce temporally coherent controls that effectively overcome stiction by recombining structured primitives from the training prior.
By Eric Aislan Antonelo
arXiv:2606. 15359v1 Announce Type: new Abstract: Diffusion models have emerged as powerful tools for planning and control by learning multimodal distributions over actions and trajectories.
By Paolo Giaretta, Zeyang Li, Navid Azizan
arXiv:2607. 24051v1 Announce Type: cross Abstract: Low-thrust trajectory optimization is a core technology in deep-space mission design.
By An-yi Huang
arXiv:2606. 11019v1 Announce Type: cross Abstract: Learning-based motion planners, despite recent progress, often suffer from temporal inconsistency.
By Zehan Zhang, Neng Zhang, Yaoyi Li, Jia Cai, Zhiling Wang
arXiv:2605. 26790v3 Announce Type: replace Abstract: Low-thrust trajectory design relies heavily on repeated evaluations of fuel consumption and transfer feasibility, which require expensive optimal control solutions.
By Zhong Zhang, Giacomo Acciarini, Dario Izzo, Hexi Baoyin, Francesco Topputo
arXiv:2507.06625v4 Announce Type: replace-cross
Abstract: Model Predictive Control (MPC) enables reliable trajectory optimization under dynamics constraints, but often depends on accurate dynamics mo...
By Shizhe Cai, Zeya Yin, Jayadeep Jacob, Fabio Ramos
arXiv:2607. 21644v1 Announce Type: new Abstract: We present a goal-agnostic control framework for partial differential equations (PDEs) built around a joint-embedding predictive architecture (JEPA).
By Jonathan Gallagher, Roberto Guglielmi
arXiv:2602. 05533v3 Announce Type: replace Abstract: We study conditional generation in diffusion models under hard constraints, where generated samples must satisfy prescribed events with probability one.
By Zhengyi Guo, Wenpin Tang, Renyuan Xu
arXiv:2608. 02487v1 Announce Type: cross Abstract: Recently, rectified flow has emerged as a fundamental framework for large-scale image generation, powering state-of-the-art systems such as FLUX.
By Leda Wang, Zhehao Xu, Qiang Liu, Harrison H. Zhou
arXiv:2512. 08280v3 Announce Type: replace-cross Abstract: Offline decision-making via diffusion models often produces trajectories that are misaligned with system dynamics, limiting their reliability for control.
By Haldun Balim, Na Li, Yilun Du