The paper introduces a curriculum-driven deep reinforcement learning framework for autonomous far-range rocket landing. It uses a three-stage curriculum, critic warm‑up, rehearsal to avoid catastrophic forgetting, and a high‑dimensional observation vector with a full 3×3 rotation matrix. The redesigned reward function bounds per‑step rewards and accumulates progress, yielding a 99.98% success rate over 100,000 episodes, far surpassing the baseline.
By Atin Chowdhury, Piyush Joshi
arXiv:2607. 01528v1 Announce Type: new Abstract: Small multirotor aircraft are increasingly tasked with operations in the atmospheric boundary layer, where turbulent winds comparable to the vehicle's airspeed degrade trajectory tracking and can defeat conventional feedback control.
By Abdullah Al Tasim, Wei Sun
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 presents a curriculum‑based adversarial heterogeneous agent reinforcement learning (HARL‑AC) approach for autonomous quad‑copter landing on a ship deck in maritime settings. Using Heterogeneous‑Agent Proximal Policy Optimization (HAPPO) in NVIDIA Isaac Lab, the authors train a cooperative control policy that outperforms domain‑randomized baselines, achieving up to 97.5% success on in‑distribution sea states and higher median success and lower crash rates on out‑of‑distribution sea states. The adversarially trained policy also exhibits more cautious behavior, slightly increasing timeouts but improving safety in severe, unseen conditions.
By Allan Minh-Tam Nguyen, Sree Showrya Kotala, Stefan Banioi-Crijman, Kurt Driessens, Rico M\"ockel
arXiv:2606. 31291v1 Announce Type: new Abstract: Deep reinforcement learning has the potential to solve attitude control problems more adaptively, precisely, and robustly by handling nonlinear dynamics, uncertainties, and failure cases more effectively than traditional attitude control approaches.
By Alexander Fabisch, Melvin Laux, Mariela De Lucas \'Alvarez, Edoardo Caroselli, Julian Theis
The paper presents a method for improving the robustness of deep reinforcement learning (DRL) policies used in planetary landing by employing adversarial reinforcement learning. A secondary agent generates physically constrained perturbations that challenge the landing controller, creating an asymmetric zero‑sum game. Training is conducted in three stages—baseline learning, stochastic robustness enhancement, and iterative adversarial optimization—showing that adversarial disturbances uncover vulnerabilities missed by conventional randomization and hint at better generalization to unseen perturbations.
By Miguel Leiva-V\'elez, Nicolas Gaston-Rozado, Andrea Scorsoglio, Hodei Urrutxua, V\'ictor Rodr\'iguez-Fern\'andez