arXiv Machine Learning

ARMOR: Robust Reinforcement Learning-based Control for UAVs under Physical Attacks

arXiv:2506. 22423v2 Announce Type: replace Abstract: Unmanned Aerial Vehicles (UAVs) depend on onboard sensors for perception, navigation, and control.

arXiv AI

Jun 3

Curriculum-Adapted Robust Reinforcement Learning for UAV Deconfliction in Adversarial Environments

arXiv:2506. 21129v2 Announce Type: replace-cross Abstract: Autonomous unmanned aerial vehicles (UAVs) increasingly rely on reinforcement learning (RL) for navigation.

By Deepak Kumar Panda, Adolfo Perrusquia, Weisi Guo

agents reinforcement-learning safety

arXiv AI

Jul 3

Lightweight Safe Reinforcement Learning for End-to-End UAV Navigation

arXiv:2607. 01794v1 Announce Type: cross Abstract: With the rapid development of autonomous aerial systems, Unmanned Aerial Vehicles (UAVs) are increasingly deployed in applications such as inspection, environmental monitoring, and rescue, creating growing demand for reliable autonomous navigation.

By Shenghui Zhang, YuXuan Gao, Songwei Zhao, Jifeng Hu, Zijing Zhang, Hechang Chen

agents reinforcement-learning safety

arXiv AI

Jun 30

RoAd-RL: A Unified Library and Benchmark for Robust Adversarial Reinforcement Learning

arXiv:2606. 29867v1 Announce Type: cross Abstract: Deep Reinforcement Learning (DRL) has achieved significant success in robotics and autonomous systems, yet remains vulnerable to adversarial perturbations that can severely degrade performance.

By Adithya Mohan, Daniel Kriegl, Torsten Sch\"on

agents reinforcement-learning robotics benchmarks safety

Hugging Face Trending Papers

Jul 26

Anticipatory Risk-Guided Reinforcement Learning for Safe Flight Through Dynamic Clutter

Safe quadrotor navigation in cluttered and dynamic environments depends not only on instantaneous geometric perception, but more critically on anticipating collision risks induced by relative motion. Conventional modular pipelines frequently suffer from perception latency, while end-to-end learning methods relying on implicit scalar rewards often struggle to extract reliable spatio-temporal features without physics-grounded supervision.

computer-vision reinforcement-learning robotics safety

arXiv AI

Jun 10

Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey

arXiv:2403. 00420v3 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) is a subfield of machine learning for training autonomous agents that take sequential actions across complex environments.

By Lucas Schott, Josephine Delas, Hatem Hajri, Elies Gherbi, Reda Yaich, Nora Boulahia-Cuppens, Frederic Cuppens, Sylvain Lamprier

agents reinforcement-learning safety

arXiv Machine Learning

Jul 28

Anticipatory Risk-Guided Reinforcement Learning for Safe Flight Through Dynamic Clutter

arXiv:2607. 23565v1 Announce Type: cross Abstract: Safe quadrotor navigation in cluttered and dynamic environments depends not only on instantaneous geometric perception, but more critically on anticipating collision risks induced by relative motion.

By Yuchao Mei, Guohao Zhang, Luxia Ai, Haopeng Chen, Wenbing Tao

computer-vision reinforcement-learning robotics safety

arXiv AI

Jun 8

Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach

arXiv:2510. 09041v3 Announce Type: replace-cross Abstract: Deep reinforcement learning (DRL) has demonstrated remarkable success in developing autonomous driving policies.

By Junchao Fan, Qi Wei, Ruichen Zhang, Yang Lu, Jianhua Wang, Xiaolin Chang, Bo Ai

agents reinforcement-learning robotics benchmarks safety

arXiv AI

Aug 12

Threat-guided Policy-aware Scene Perturbation for Safe Autonomous Driving with Online Reinforcement Learning

arXiv:2608. 10403v1 Announce Type: new Abstract: Reinforcement learning (RL) has shown promising performance in autonomous driving, yet ensuring the safety of online RL policies remains challenging due to insufficient exposure to safety-critical driving scenes.

By Xincong Hu (Nanjing University), Lei Ou (Nanjing University), Maosen Li (Yinwang Intelligent Technology Co., Ltd), Jingtao Zhang (Yinwang Intelligent Technology Co., Ltd), Liguo Hou (Yinwang Intelligent Technology Co., Ltd), Zongzhang Zhang (Nanjing University)

agents reinforcement-learning robotics benchmarks safety

arXiv Machine Learning

Jul 9

Safe Reinforcement Learning using Ideas from Model Predictive Control

arXiv:2607. 07252v1 Announce Type: new Abstract: Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics.

By Georg Sch\"afer, Jakob Rehrl, Stefan Huber, Simon Hirlaender

agents reinforcement-learning robotics safety

arXiv Machine Learning

Aug 5

Adversarial observations in probabilistic State-Space Models for robust Reinforcement Learning

arXiv:2606. 20880v2 Announce Type: replace-cross Abstract: Decision-making under partial or adversarial observability requires accurate inference of the environment's latent state and its associated uncertainty.

By M. Santos-Pascual, D. R\'ios Insua

agents reinforcement-learning robotics safety

arXiv AI

Jun 6

Adversarial Agents: Black-Box Evasion Attacks with Reinforcement Learning

arXiv:2503. 01734v3 Announce Type: replace-cross Abstract: Attacks on machine learning models have been extensively studied through stateless optimization.

By Kyle Domico, Jean-Charles Noirot Ferrand, Ryan Sheatsley, Eric Pauley, Josiah Hanna, Patrick McDaniel

agents computer-vision reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 16

Distributionally Robust and Safe Imitation Learning

arXiv:2607. 13436v1 Announce Type: new Abstract: Imitation learning (IL) has achieved remarkable success in complex decision-making tasks.

By Ahmed Aboudonia, Naira Hovakimyan

safety

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