3 citations · 3 across the 10 of their papers we have counts for
11 papers
Explaining Reinforcement Learning Agents via Inductive Logic Programming
Celeste Veronese, Edoardo Zorzi, Daniele Meli +1
Explainable Reinforcement Learning (XRL) seeks to make Reinforcement Learning (RL) policies more transparent and interpretable, a key requirement in safety-critical and human-centr…
Robust Scene Transfer for PointGoal Navigation via Privileged Sensor Guided Contrastive Learning
Amirhossein Zhalehmehrabi, Tiziano Tezze, Alberto Castelini +1
We propose a sensor-guided adaptive contrastive learning framework for visual representation learning in PointGoal navigation. During training, privileged LiDAR sensing guides the…
Sample-Efficient Neurosymbolic Deep Reinforcement Learning
Celeste Veronese, Alessandro Farinelli, Daniele Meli
Reinforcement Learning (RL) is a well-established framework for sequential decision-making in complex environments. However, state-of-the-art Deep RL (DRL) algorithms typically req…
On the Probabilistic Learnability of Compact Neural Network Preimage Bounds
Luca Marzari, Manuele Bicego, Ferdinando Cicalese +1
Although recent provable methods have been developed to compute preimage bounds for neural networks, their scalability is fundamentally limited by the #P-hardness of the problem. I…
Probabilistically Tightened Linear Relaxation-based Perturbation Analysis for Neural Network Verification
Luca Marzari, Ferdinando Cicalese, Alessandro Farinelli
We present robabilistically ightened near elaxation-based erturbation nalysis (), a nove…
Advancing Neural Network Verification through Hierarchical Safety Abstract Interpretation
Luca Marzari, Isabella Mastroeni, Alessandro Farinelli
Traditional methods for formal verification (FV) of deep neural networks (DNNs) are constrained by a binary encoding of safety properties, where a model is classified as either saf…
Learning Symbolic Persistent Macro-Actions for POMDP Solving Over Time
Celeste Veronese, Daniele Meli, Alessandro Farinelli
This paper proposes an integration of temporal logical reasoning and Partially Observable Markov Decision Processes (POMDPs) to achieve interpretable decision-making under uncertai…
Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation
Luca Marzari, Francesco Trotti, Enrico Marchesini +1
Achieving safe autonomous navigation systems is critical for deploying robots in dynamic and uncertain real-world environments. In this paper, we propose a hierarchical control fra…
Depth-Constrained ASV Navigation with Deep RL and Limited Sensing
Amirhossein Zhalehmehrabi, Daniele Meli, Francesco Dal Santo +2
Autonomous Surface Vehicles (ASVs) play a crucial role in maritime operations, yet their navigation in shallow-water environments remains challenging due to dynamic disturbances an…
Monte Carlo Tree Search with Velocity Obstacles for safe and efficient motion planning in dynamic environments
Lorenzo Bonanni, Daniele Meli, Alberto Castellini +1
Online motion planning is a challenging problem for intelligent robots moving in dense environments with dynamic obstacles, e.g., crowds. In this work, we propose a novel approach…
Online inductive learning from answer sets for efficient reinforcement learning exploration
Celeste Veronese, Daniele Meli, Alessandro Farinelli
This paper presents a novel approach combining inductive logic programming with reinforcement learning to improve training performance and explainability. We exploit inductive lear…