arXiv Machine Learning July 7, 2026 By Zhenbo Wang

Optimality-Informed Neural Networks for Lunar Landing Trajectory Optimization

Read the original on arXiv Machine Learning →

arXiv:2607. 02741v1 Announce Type: cross Abstract: This paper develops an Optimality-Informed Neural Network (OINN) approach for the energy-optimal, free-final-time powered descent of a lunar lander from any initial position, velocity, and mass within a bounded operating envelope to a fixed landing site with zero terminal velocity.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 16

Dual-Network PINNs for Optimal Control: A Reproducible Benchmark on the Mass-Spring-Damper System

arXiv:2606. 15271v1 Announce Type: cross Abstract: This work presents a transparent and reproducible benchmark study of a direct dual-network Physics-Informed Neural Network (PINN) formulation for the optimal control of a mass-spring-damper system.

By Abdeladhim Tahimi, Rinaldo Vieira da Silva Junior

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arXiv AI

Jul 14

Average-Power-Budgeted Underwater Vehicle Control via Constrained Reinforcement Learning

arXiv:2606. 25680v2 Announce Type: replace-cross Abstract: Underwater vehicles operate from a fixed onboard energy budget that propulsion rapidly depletes, so a controller that completes its task while drawing less thruster power directly extends mission range and endurance.

By Yinuo Wang, Gavin Tao, Yuze Liu, John V. Ringwood

reinforcement-learning

arXiv Machine Learning

Jun 30

Pretrained Approximators for Low-Thrust Trajectory Cost and Reachability

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 Machine Learning

Jul 20

Physics-enhanced reinforcement learning for real-time optimal control of dynamical systems

arXiv:2607. 16177v1 Announce Type: new Abstract: Reinforcement learning (RL) has recently emerged as a promising feedback control strategy for nonlinear and complex dynamical systems.

By Matteo Tomasetto, Nicol\`o Botteghi, Gabriele Bruni, Andrea Manzoni

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arXiv Machine Learning

Jun 8

Learning All-Terrain Locomotion for a Planetary Rover with Actively Articulated Suspension

arXiv:2606. 06790v1 Announce Type: cross Abstract: This paper presents ERNEST, a four-wheeled planetary rover concept equipped with a two-degree-of-freedom Active Gimbal Suspension that combines yaw and roll actuation to enable wheel reconfiguration, steering, and active load redistribution.

By Arthur Bouton, Tristan D. Hasseler, Michael Paton, Travis Brown, Jacob Levy, William Reid, Joshua Martin, Hari Nayar

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arXiv Machine Learning

Jul 27

Toward Goal-Agnostic Joint-Embedding Predictive Control of Partial Differential Equations

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

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Dual-Network PINNs for Optimal Control: A Reproducible Benchmark on the Mass-Spring-Damper System

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