52 citations · 112 across the 27 of their papers we have counts for
8 papers · 1 filter
Language-enhanced RNR-Map: Querying Renderable Neural Radiance Field maps with natural language
Francesco Taioli, Federico Cunico, Federico Girella +3
We present Le-RNR-Map, a Language-enhanced Renderable Neural Radiance map for Visual Navigation with natural language query prompts. The recently proposed RNR-Map employs a grid st…
Enumerating Safe Regions in Deep Neural Networks with Provable Probabilistic Guarantees
Luca Marzari, Davide Corsi, Enrico Marchesini +2
Identifying safe areas is a key point to guarantee trust for systems that are based on Deep Neural Networks (DNNs). To this end, we introduce the AllDNN-Verification problem: given…
Learning Logic Specifications for Soft Policy Guidance in POMCP
Giulio Mazzi, Daniele Meli, Alberto Castellini +1
Partially Observable Monte Carlo Planning (POMCP) is an efficient solver for Partially Observable Markov Decision Processes (POMDPs). It allows scaling to large state spaces by com…
Unsupervised Active Visual Search with Monte Carlo planning under Uncertain Detections
Francesco Taioli, Francesco Giuliari, Yiming Wang +6
We propose a solution for Active Visual Search of objects in an environment, whose 2D floor map is the only known information. Our solution has three key features that make it more…
Constrained Reinforcement Learning and Formal Verification for Safe Colonoscopy Navigation
Davide Corsi, Luca Marzari, Ameya Pore +4
The field of robotic Flexible Endoscopes (FEs) has progressed significantly, offering a promising solution to reduce patient discomfort. However, the limited autonomy of most robot…
Safe Deep Reinforcement Learning by Verifying Task-Level Properties
Enrico Marchesini, Luca Marzari, Alessandro Farinelli +1
Cost functions are commonly employed in Safe Deep Reinforcement Learning (DRL). However, the cost is typically encoded as an indicator function due to the difficulty of quantifying…
Online Safety Property Collection and Refinement for Safe Deep Reinforcement Learning in Mapless Navigation
Luca Marzari, Enrico Marchesini, Alessandro Farinelli
Safety is essential for deploying Deep Reinforcement Learning (DRL) algorithms in real-world scenarios. Recently, verification approaches have been proposed to allow quantifying th…
The #DNN-Verification Problem: Counting Unsafe Inputs for Deep Neural Networks
Luca Marzari, Davide Corsi, Ferdinando Cicalese +1
Deep Neural Networks are increasingly adopted in critical tasks that require a high level of safety, e.g., autonomous driving. While state-of-the-art verifiers can be employed to c…