arXiv AI

Interval POMDP Shielding for Imperfect-Perception Agents

The paper introduces Interval POMDP Shielding for agents with imperfect perception, aiming to prevent unsafe actions when sensor readings may be misclassified. By estimating perception uncertainty from finite labeled data, the authors construct confidence intervals and model the system as a finite Interval Partially Observable Markov Decision Process. They propose an algorithm that computes a conservative belief set, enabling a runtime shield that guarantees, with high probability, that any action allowed by the shield meets a specified safety lower bound. Experiments on four case studies demonstrate that this shielding approach outperforms state‑of‑the‑art baselines in safety.

arXiv AI

Jun 2

Robust Shielding for Safe Reinforcement Learning

arXiv:2606. 00270v1 Announce Type: new Abstract: Shielding is an effective approach to formally guarantee the safety of reinforcement learning agents in Markov decision processes (MDPs).

By Edwin Hamel-De le Court, Thom Badings, Alessandro Abate, Francesco Belardinelli, Francesco Fabiano

agents reinforcement-learning safety

arXiv AI

Aug 21

Adaptive Probabilistic Shielding by Learning MDPs for Safe Reinforcement Learning

arXiv:2608. 19836v1 Announce Type: cross Abstract: Probabilistic shielding is a technique for safe reinforcement learning (RL).

By Astrid Horn Brorholt (Aalborg University, Aalborg, Denmark), Maris F. L. Galesloot (Radboud University, Nijmegen, Netherlands), Nils Jansen (Radboud University, Nijmegen, Netherlands), Kim Guldstrand Larsen (Aalborg University, Aalborg, Denmark), Christian Schilling (Aalborg University, Aalborg, Denmark)

agents reinforcement-learning safety

arXiv Machine Learning

Sep 22

Correcting Learning-based Perception for Safety

The paper presents a two-step method to correct machine‑learning based perception for safety in autonomous systems. First, it uses offline computation to characterize uncertainties from the ML module via preimages of perception contracts. Then, at runtime, a risk heuristic selects specific states from these uncertain estimates to guide control decisions, reducing safety violations in adaptive cruise control scenarios while adding minimal delay.

By Yan Miao, Hussein Darir, Sayan Mitra

agents computer-vision safety

arXiv AI

Jun 11

Planning under Distribution Shifts with Causal POMDPs

arXiv:2602. 23545v2 Announce Type: replace Abstract: In the real world, planning is often challenged by distribution shifts.

By Matteo Ceriscioli, Karthika Mohan

reinforcement-learning

arXiv Machine Learning

Jun 4

Explainably Safe Reinforcement Learning

arXiv:2606. 04634v1 Announce Type: new Abstract: Trust in a decision-making system requires both safety guarantees and the ability to interpret and understand its behavior.

By Sabine Rieder, Stefan Pranger, Debraj Chakraborty, Jan K\v{r}et\'insk\'y, Bettina K\"onighofer

reinforcement-learning safety

arXiv AI

6d ago

Risk-Averse Online POMDP Planning via CVaR of the Immediate Cost with Performance Guarantees

arXiv:2609.35874v1 Announce Type: new Abstract: Online POMDP planners optimize the expected cumulative cost, which can mask dangerous states when the belief places significant mass on high-cost state...

By Yaacov Pariente, Vadim Indelman

arXiv Machine Learning

Sep 15

Safe Meta-Reinforcement Learning via Information Space Reachability

arXiv:2609.15915v1 Announce Type: new Abstract: Meta-reinforcement learning (meta-RL) enables agents to adapt to unseen tasks with limited experience. Despite its promise, the application of meta-RL...

By Zeyang Li, Sunbochen Tang, Navid Azizan

agents reinforcement-learning benchmarks safety

arXiv AI

Jun 2

Permissive Safety Through Trusted Inference: Verifiable Belief-Space Neural Safety Filters for Assured Interactive Robotics

arXiv:2606. 02562v1 Announce Type: cross Abstract: Autonomous robots that interact with people must make safe and efficient decisions under human-induced uncertainty, such as their preferences, goals, competency, and willingness to cooperate.

By Haimin Hu

agents robotics benchmarks safety

arXiv Machine Learning

Jun 2

All Models are Wrong, Knowing Where is Useful: On Model Uncertainty in Reinforcement Learning

arXiv:2606. 01363v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) infers information about the environment from a learned dynamics model and bears the potential to address open problems such as data efficient and safe learning in robotics.

By Bernd Frauenknecht, Devdutt Subhasish, Artur Eisele, Friedrich Solowjow, Sebastian Trimpe

agents reinforcement-learning robotics

arXiv Machine Learning

Aug 3

Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions

arXiv:2307. 10524v3 Announce Type: replace Abstract: We study the tradeoff between consistency and robustness in the context of a single-trajectory time-varying Markov Decision Process (MDP) with untrusted machine-learned advice.

By Tongxin Li, Yiheng Lin, Shaolei Ren, Adam Wierman

reinforcement-learning

arXiv AI

Jul 22

Toward Learning POMDPs Beyond Full-Rank Actions and State Observability

arXiv:2601. 18930v4 Announce Type: replace-cross Abstract: We are interested in enabling autonomous agents to learn and reason about systems with hidden states, such as locking mechanisms.

By Seiji Shaw, Travis Manderson, Chad Kessens, Nicholas Roy

agents reinforcement-learning

arXiv AI

Jul 7

Finite Reliability Representations: Noise-Calibrated Belief-Space Covers for Reliable Decision-Making

arXiv:2607. 04019v1 Announce Type: cross Abstract: Physical sensing and actuation noise floors should inform how much belief resolution a decision-making system can reliably use.

By Hyung-Jin Yoon, Hunmin Kim

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