arXiv Machine Learning

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.

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 Machine Learning

Aug 4

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.

By Pritam Dash, Ethan Chan, Nathan P. Lawrence, Karthik Pattabiraman

reinforcement-learning robotics safety

arXiv AI

Jun 10

SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration

arXiv:2606. 10228v1 Announce Type: cross Abstract: Safe exploration is a prerequisite for deploying reinforcement learning (RL) agents in safety-critical domains.

By Kaustubh Mani, Yann Pequignot, Vincent Mai, Liam Paull

agents reinforcement-learning safety

arXiv AI

Jul 29

A context-adaptive policy framework for robust and reactive robotic manipulation via uncertainty-aware imitation learning

arXiv:2410. 24035v2 Announce Type: replace-cross Abstract: Generating robust and reactive manipulation strategies that can adapt to changing context information is a challenging task in robotics.

By Tim R. Winter, Leonard Kl\"upfel, Ashok M. Sundaram, Werner Friedl, Maximo A. Roa, Freek Stulp, Jo\~ao Silv\'erio

robotics benchmarks

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 Machine Learning

Aug 11

SAFE-CHEM: Uncertainty-Aware Policy Switching for Robust Robotic Chemistry

arXiv:2608. 09303v1 Announce Type: cross Abstract: The deployment of autonomous robotic systems in chemistry laboratories is accelerating experimental workflows and providing the foundational data for AI-driven scientific discovery.

By Laura Jones, Shazil Shahzad, Ayesha Sana, Gabriella Pizzuto

agents robotics safety

arXiv Machine Learning

Jul 9

Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees

arXiv:2405. 16668v2 Announce Type: replace Abstract: Adversarial Imitation Learning (AIL) faces challenges with sample inefficiency because of its reliance on sufficient on-policy data to evaluate the performance of the current policy during reward function updates.

By Yilei Chen, Vittorio Giammarino, James Queeney, Ioannis Ch. Paschalidis

safety

arXiv AI

Jun 11

Noise-Guided Transport for Imitation Learning

arXiv:2509. 26294v2 Announce Type: replace-cross Abstract: We consider imitation learning in the low-data regime, where only a limited number of expert demonstrations are available.

By Lionel Blond\'e, Joao A. Candido Ramos, Alexandros Kalousis

safety

arXiv AI

1d ago

Quantifying Risk Under Evolving Uncertainty: Belief-Dependent Robustness for Safe Sequential Decision Making

arXiv:2608. 17574v1 Announce Type: new Abstract: How cautious should an agent be while it is still learning its environment?

By Deep Kumar Ganguly, Jan Kretinsky

llms agents safety

arXiv AI

Jun 18

Robust Regularized Policy Iteration under Transition Uncertainty

arXiv:2603. 09344v3 Announce Type: replace Abstract: Offline reinforcement learning (RL) enables data-efficient and safe policy learning without online exploration, but its performance often degrades under distribution shift.

By Hongqiang Lin, Zhenghui Fu, Weihao Tang, Pengfei Wang, Yiding Sun, Qixian Huang, Dongxu Zhang

reinforcement-learning benchmarks

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 7

Integrating Physics-Informed Neural Networks for Safe Reinforcement Learning in a 1-DoF Helicopter System

arXiv:2607. 03125v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) offers powerful control for industrial cyber-physical systems (ICPSs), but its "black-box" exploration risks violating strict hardware safety limits.

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

reinforcement-learning safety

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