5 citations · 13 across the 11 of their papers we have counts for
12 papers
Self-Paced Curriculum Reinforcement Learning for Autonomous Superbike Racing in Simulation
Luca Ghisi, Jacopo Essenziale, Carlo D'Eramo +1
Autonomous Racing has seen remarkable progress through deep Reinforcement Learning (RL), primarily for four-wheeled vehicles. However, motorbikes introduce substantially greater co…
Instance-Guided Unsupervised Domain Adaptation for Robotic Semantic Segmentation
Michele Antonazzi, Lorenzo Signorelli, Matteo Luperto +1
Semantic segmentation networks, which are essential for robotic perception, often suffer from performance degradation when the visual distribution of the deployment environment dif…
Biasing Frontier-Based Exploration with Saliency Areas
Matteo Luperto, Valerii Stakanov, Giacomo Boracchi +2
Autonomous exploration is a widely studied problem where a robot incrementally builds a map of a previously unknown environment. The robot selects the next locations to reach using…
Robust Localization, Mapping, and Navigation for Quadruped Robots
Dyuman Aditya, Junning Huang, Nico Bohlinger +5
Quadruped robots are currently a widespread platform for robotics research, thanks to powerful Reinforcement Learning controllers and the availability of cheap and robust commercia…
Estimating Map Completeness in Robot Exploration
Matteo Luperto, Marco Maria Ferrara, Giacomo Boracchi +1
In this paper, we propose a method that, given a partial grid map of an indoor environment built by an autonomous mobile robot, estimates the amount of the explored area represente…
Frontier-Based Exploration for Multi-Robot Rendezvous in Communication-Restricted Unknown Environments
Mauro Tellaroli, Matteo Luperto, Michele Antonazzi +1
Multi-robot rendezvous and exploration are fundamental challenges in the domain of mobile robotic systems. This paper addresses multi-robot rendezvous within an initially unknown e…
R2SNet: Scalable Domain Adaptation for Object Detection in Cloud-Based Robotic Ecosystems via Proposal Refinement
Michele Antonazzi, Matteo Luperto, N. Alberto Borghese +1
We introduce a novel approach for scalable domain adaptation in cloud robotics scenarios where robots rely on third-party AI inference services powered by large pre-trained deep ne…
Development and Adaptation of Robotic Vision in the Real-World: the Challenge of Door Detection
Michele Antonazzi, Matteo Luperto, N. Alberto Borghese +1
Mobile service robots are increasingly prevalent in human-centric, real-world domains, operating autonomously in unconstrained indoor environments. In such a context, robotic visio…
Robust Structure Identification and Room Segmentation of Cluttered Indoor Environments from Occupancy Grid Maps
Matteo Luperto, Tomasz Piotr Kucner, Andrea Tassi +2
Identifying the environment's structure, i.e., to detect core components as rooms and walls, can facilitate several tasks fundamental for the successful operation of indoor autonom…
Enhancing Door-Status Detection for Autonomous Mobile Robots during Environment-Specific Operational Use
Michele Antonazzi, Matteo Luperto, Nicola Basilico +1
Door-status detection, namely recognizing the presence of a door and its status (open or closed), can induce a remarkable impact on a mobile robot's navigation performance, especia…
Predicting Performance of SLAM Algorithms
Matteo Luperto, Valerio Castelli, Francesco Amigoni
Among the abilities that autonomous mobile robots should exhibit, map building and localization are definitely recognized as fundamental. Consequently, countless algorithms for sol…
Exploration of Indoor Environments Predicting the Layout of Partially Observed Rooms
Matteo Luperto, Luca Fochetta, Francesco Amigoni
We consider exploration tasks in which an autonomous mobile robot incrementally builds maps of initially unknown indoor environments. In such tasks, the robot makes a sequence of d…