19 papers
The contribution of Soviet scientists to the emergence of high-energy neutrino astrophysics: from the Cherenkov effect to underwater and in-ice telescopes
Bakhromzod Rizoi
The 2026 Nobel Prize in Physics was awarded to Francis Halzen @for decisive contributions to the IceCube Neutrino Observatory and the discovery of high-energy neutrinos of astrophy…
One Frame, Full Heartbeat: ECG-Free Cardiac Cine MRI Synthesis via Phase-Conditioned Flow Matching
Shiyi Wang, Ruochen Sun, Peirong Liu +2
Cine cardiovascular magnetic resonance (CMR) analysis relies on multi-frame sequences capturing the full cardiac cycle. However, standard multi-frame acquisition depends heavily on…
Vision--Language Signals in Constrained RL: Safety Gains Without Anticipation
Samuel Tetteh, Cody Fleming
Safe reinforcement learning seeks policies that maximise task performance while satisfying safety constraints. In driving benchmarks, however, collision costs typically appear only…
A Mean-Field Approach for Safe Routing of Multi-Destination Urban Air Mobility Networks
Nameer Fawwaz Ahmed, Cody Fleming, Yasser Shoukry
As Urban Air Mobility (UAM) systems scale toward high-density operations, managing autonomous Unmanned Aerial Vehicle (UAV) traffic requires control frameworks that are both tracta…
Inverting Self-Triggered Control: Adversarial Reinforcement Learning for Sparse Denial-of-Service Attacks
Adam Haroon, Erick J. Rodríguez-Seda, Tristan Schuler +1
Self-triggered reinforcement learning control (RL-STC) learns the sparsest control schedule that preserves Lyapunov-decreasing stability under a Run-Time Assurance (RTA) override.…
Certified Safety Curation: Distribution-Free Guarantees for Safe Offline Reinforcement Learning
Adam Haroon, Cody Fleming
Safe offline reinforcement learning assumes a cost function on every transition. We ask what remains possible when safety can be judged only by comparing short clips and occasional…
Who Analyses the Analyser? Self-Validating LLM Hazard Analysis with Constitutional Meta-STPA
Samuel Tetteh, Udip Shrestha, Joshua R. Waite +1
Large language models (LLMs) are increasingly trusted to draft the artifacts of safety analysis such as, losses, hazards, Unsafe Control Actions (UCAs), and safety constraints, ins…
Seeing Before Colliding: Anticipatory Safe RL with Frozen Vision-Language Models
Samuel Tetteh, Cody Fleming
The cost signal that constrained-RL algorithms optimize against is almost always reactive: the simulator emits a non-zero cost only after a collision has begun, and the Lagrange mu…
COOPO: Cyclic Offline-Online Policy Optimization Algorithm
Qisai Liu, Zhanhong Jiang, Joshua Russell Waite +3
Offline reinforcement learning struggles with distributional shift and constrained performance due to static dataset limitations, while online RL demands prohibitive environment in…
Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance
Adam Haroon, Erick J. Rodríguez-Seda, Cody Fleming +1
Safe reinforcement learning (RL) typically asks an agent should do. We ask it needs to act, and show that a single policy can jointly learn control…
LexiSafe: Offline Safe Reinforcement Learning with Lexicographic Safety-Reward Hierarchy
Hsin-Jung Yang, Zhanhong Jiang, Prajwal Koirala +3
Offline safe reinforcement learning (RL) is increasingly important for cyber-physical systems (CPS), where safety violations during training are unacceptable and only pre-collected…
LCLA: Language-Conditioned Latent Alignment for Vision-Language Navigation
Nitesh Subedi, Adam Haroon, Samuel Tetteh +3
We propose LCLA (Language-Conditioned Latent Alignment), a framework for vision-language navigation that learns modular perception-action interfaces by aligning sensory observation…
Certified Learning-Enabled Noise-Aware Motion Planning for Urban Air Mobility
Jaejeong Park, Mahmoud Elfar, Cody Fleming +1
Urban Air Mobility (UAM) has emerged as a promising solution to alleviate urban congestion and transportation challenges. Nevertheless, the noise generated by eVTOL aircrafts poses…
Can Pretrained Vision-Language Embeddings Alone Guide Robot Navigation?
Nitesh Subedi, Adam Haroon, Shreyan Ganguly +4
Foundation models have revolutionized robotics by providing rich semantic representations without task-specific training. While many approaches integrate pretrained vision-language…
Flow-Based Single-Step Completion for Efficient and Expressive Policy Learning
Prajwal Koirala, Cody Fleming
Generative models such as diffusion and flow-matching offer expressive policies for offline reinforcement learning (RL) by capturing rich, multimodal action distributions, but thei…
FAWAC: Feasibility Informed Advantage Weighted Regression for Persistent Safety in Offline Reinforcement Learning
Prajwal Koirala, Zhanhong Jiang, Soumik Sarkar +1
Safe offline reinforcement learning aims to learn policies that maximize cumulative rewards while adhering to safety constraints, using only offline data for training. A key challe…
Latent Safety-Constrained Policy Approach for Safe Offline Reinforcement Learning
Prajwal Koirala, Zhanhong Jiang, Soumik Sarkar +1
In safe offline reinforcement learning (RL), the objective is to develop a policy that maximizes cumulative rewards while strictly adhering to safety constraints, utilizing only of…
F1tenth Autonomous Racing With Offline Reinforcement Learning Methods
Prajwal Koirala, Cody Fleming
Autonomous racing serves as a critical platform for evaluating automated driving systems and enhancing vehicle mobility intelligence. This work investigates offline reinforcement l…
Solving Offline Reinforcement Learning with Decision Tree Regression
Prajwal Koirala, Cody Fleming
This study presents a novel approach to addressing offline reinforcement learning (RL) problems by reframing them as regression tasks that can be effectively solved using Decision…