52 citations · 112 across the 27 of their papers we have counts for
7 papers · 1 filter
Curriculum Learning for Safe Mapless Navigation
Luca Marzari, Davide Corsi, Enrico Marchesini +1
This work investigates the effects of Curriculum Learning (CL)-based approaches on the agent's performance. In particular, we focus on the safety aspect of robotic mapless navigati…
Benchmarking Safe Deep Reinforcement Learning in Aquatic Navigation
Enrico Marchesini, Davide Corsi, Alessandro Farinelli
We propose a novel benchmark environment for Safe Reinforcement Learning focusing on aquatic navigation. Aquatic navigation is an extremely challenging task due to the non-stationa…
Centralizing State-Values in Dueling Networks for Multi-Robot Reinforcement Learning Mapless Navigation
Enrico Marchesini, Alessandro Farinelli
We study the problem of multi-robot mapless navigation in the popular Centralized Training and Decentralized Execution (CTDE) paradigm. This problem is challenging when each robot…
Safe Reinforcement Learning using Formal Verification for Tissue Retraction in Autonomous Robotic-Assisted Surgery
Ameya Pore, Davide Corsi, Enrico Marchesini +4
Deep Reinforcement Learning (DRL) is a viable solution for automating repetitive surgical subtasks due to its ability to learn complex behaviours in a dynamic environment. This tas…
POMP++: Pomcp-based Active Visual Search in unknown indoor environments
Francesco Giuliari, Alberto Castellini, Riccardo Berra +5
In this paper we focus on the problem of learning online an optimal policy for Active Visual Search (AVS) of objects in unknown indoor environments. We propose POMP++, a planning s…
Rule-based Shielding for Partially Observable Monte-Carlo Planning
Giulio Mazzi, Alberto Castellini, Alessandro Farinelli
Partially Observable Monte-Carlo Planning (POMCP) is a powerful online algorithm able to generate approximate policies for large Partially Observable Markov Decision Processes. The…
Towards Hierarchical Task Decomposition using Deep Reinforcement Learning for Pick and Place Subtasks
Luca Marzari, Ameya Pore, Diego Dall'Alba +3
Deep Reinforcement Learning (DRL) is emerging as a promising approach to generate adaptive behaviors for robotic platforms. However, a major drawback of using DRL is the data-hungr…