Alessandro Farinelli | DeepAI

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∙ 08/18/2023

Enumerating Safe Regions in Deep Neural Networks with Provable Probabilistic Guarantees

Identifying safe areas is a key point to guarantee trust for systems tha...

0 Luca Marzari, et al. ∙

∙ 08/17/2023

Language-enhanced RNR-Map: Querying Renderable Neural Radiance Field maps with natural language

We present Le-RNR-Map, a Language-enhanced Renderable Neural Radiance ma...

0 Francesco Taioli, et al. ∙

∙ 03/16/2023

Learning Logic Specifications for Soft Policy Guidance in POMCP

Partially Observable Monte Carlo Planning (POMCP) is an efficient solver...

0 Giulio Mazzi, et al. ∙

∙ 03/06/2023

Constrained Reinforcement Learning and Formal Verification for Safe Colonoscopy Navigation

The field of robotic Flexible Endoscopes (FEs) has progressed significan...

0 Davide Corsi, et al. ∙

∙ 02/20/2023

Safe Deep Reinforcement Learning by Verifying Task-Level Properties

Cost functions are commonly employed in Safe Deep Reinforcement Learning...

0 Enrico Marchesini, et al. ∙

∙ 02/13/2023

Online Safety Property Collection and Refinement for Safe Deep Reinforcement Learning in Mapless Navigation

Safety is essential for deploying Deep Reinforcement Learning (DRL) algo...

0 Luca Marzari, et al. ∙

∙ 01/17/2023

The #DNN-Verification problem: Counting Unsafe Inputs for Deep Neural Networks

Deep Neural Networks are increasingly adopted in critical tasks that req...

0 Luca Marzari, et al. ∙

∙ 06/20/2022

Constrained Reinforcement Learning for Robotics via Scenario-Based Programming

Deep reinforcement learning (DRL) has achieved groundbreaking successes ...

0 Davide Corsi, et al. ∙

∙ 05/26/2022

Verifying Learning-Based Robotic Navigation Systems

Deep reinforcement learning (DRL) has become a dominant deep-learning pa...

67 Guy Amir, et al. ∙

∙ 04/30/2022

An attention model for the formation of collectives in real-world domains

We consider the problem of forming collectives of agents for real-world ...

0 Adrià Fenoy, et al. ∙

∙ 12/23/2021

Curriculum Learning for Safe Mapless Navigation

This work investigates the effects of Curriculum Learning (CL)-based app...

0 Luca Marzari, et al. ∙

∙ 12/16/2021

Benchmarking Safe Deep Reinforcement Learning in Aquatic Navigation

We propose a novel benchmark environment for Safe Reinforcement Learning...

0 Enrico Marchesini, et al. ∙

∙ 12/16/2021

Centralizing State-Values in Dueling Networks for Multi-Robot Reinforcement Learning Mapless Navigation

We study the problem of multi-robot mapless navigation in the popular Ce...

0 Enrico Marchesini, et al. ∙

∙ 09/06/2021

Safe Reinforcement Learning using Formal Verification for Tissue Retraction in Autonomous Robotic-Assisted Surgery

Deep Reinforcement Learning (DRL) is a viable solution for automating re...

0 Ameya Pore, et al. ∙

∙ 04/28/2021

Rule-based Shielding for Partially Observable Monte-Carlo Planning

Partially Observable Monte-Carlo Planning (POMCP) is a powerful online a...

0 Giulio Mazzi, et al. ∙

∙ 02/08/2021

Towards Hierarchical Task Decomposition using Deep Reinforcement Learning for Pick and Place Subtasks

Deep Reinforcement Learning (DRL) is emerging as a promising approach to...

0 Luca Marzari, et al. ∙

∙ 12/23/2020

Identification of Unexpected Decisions in Partially Observable Monte-Carlo Planning: a Rule-Based Approach

Partially Observable Monte-Carlo Planning (POMCP) is a powerful online a...

0 Giulio Mazzi, et al. ∙

∙ 10/19/2020

Evaluating the Safety of Deep Reinforcement Learning Models using Semi-Formal Verification

Groundbreaking successes have been achieved by Deep Reinforcement Learni...

0 Davide Corsi, et al. ∙

∙ 09/17/2020

POMP: Pomcp-based Online Motion Planning for active visual search in indoor environments

In this paper we focus on the problem of learning an optimal policy for ...

0 Francesco Giuliari, et al. ∙

∙ 06/19/2018

Cooperative Queuing Policies for Effective Human-Multi-Robot Interaction

We consider multi-robot applications, where a team of robots can ask for...

0 Masoume M. Raeissi, et al. ∙

∙ 12/13/2016

Algorithms for Graph-Constrained Coalition Formation in the Real World

Coalition formation typically involves the coming together of multiple, ...

0 Filippo Bistaffa, et al. ∙

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