81 citations · 156 across the 14 of their papers we have counts for
14 papers
Heterogeneous Self-Play for Realistic Highway Traffic Simulation
Jinkai Qiu, Alessandro Saviolo, Chaojie Wang +2
Realistic highway simulation is critical for scalable safety evaluation of autonomous vehicles, particularly for interactions that are too rare to study from logged data alone. Yet…
HUNT: High-Speed UAV Navigation and Tracking in Unstructured Environments via Instantaneous Relative Frames
Alessandro Saviolo, Jeffrey Mao, Giuseppe Loianno
Search and rescue operations require unmanned aerial vehicles to both traverse unknown unstructured environments at high speed and track targets once detected. Achieving both capab…
NOVA: Navigation via Object-Centric Visual Autonomy for High-Speed Target Tracking in Unstructured GPS-Denied Environments
Alessandro Saviolo, Giuseppe Loianno
Autonomous aerial target tracking in unstructured and GPS-denied environments remains a fundamental challenge in robotics. Many existing methods rely on motion capture systems, pre…
Decentralized Nonlinear Model Predictive Control for Safe Collision Avoidance in Quadrotor Teams with Limited Detection Range
Manohari Goarin, Guanrui Li, Alessandro Saviolo +1
Multi-quadrotor systems face significant challenges in decentralized control, particularly with safety and coordination under sensing and communication limitations. State-of-the-ar…
Reactive Collision Avoidance for Safe Agile Navigation
Alessandro Saviolo, Niko Picello, Jeffrey Mao +2
Reactive collision avoidance is essential for agile robots navigating complex and dynamic environments, enabling real-time obstacle response. However, this task is inherently chall…
Learning Long-Horizon Predictions for Quadrotor Dynamics
Pratyaksh Prabhav Rao, Alessandro Saviolo, Tommaso Castiglione Ferrari +1
Accurate modeling of system dynamics is crucial for achieving high-performance planning and control of robotic systems. Although existing data-driven approaches represent a promisi…
Exploring Deep Reinforcement Learning for Robust Target Tracking using Micro Aerial Vehicles
Alberto Dionigi, Mirko Leomanni, Alessandro Saviolo +2
The capability to autonomously track a non-cooperative target is a key technological requirement for micro aerial vehicles. In this paper, we propose an output feedback control sch…
Unifying Foundation Models with Quadrotor Control for Visual Tracking Beyond Object Categories
Alessandro Saviolo, Pratyaksh Rao, Vivek Radhakrishnan +2
Visual control enables quadrotors to adaptively navigate using real-time sensory data, bridging perception with action. Yet, challenges persist, including generalization across sce…
AutoCharge: Autonomous Charging for Perpetual Quadrotor Missions
Alessandro Saviolo, Jeffrey Mao, Roshan Balu T M B +2
Battery endurance represents a key challenge for long-term autonomy and long-range operations, especially in the case of aerial robots. In this paper, we propose AutoCharge, an aut…
GaPT: Gaussian Process Toolkit for Online Regression with Application to Learning Quadrotor Dynamics
Francesco Crocetti, Jeffrey Mao, Alessandro Saviolo +2
Gaussian Processes (GPs) are expressive models for capturing signal statistics and expressing prediction uncertainty. As a result, the robotics community has gathered interest in l…
Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control
Alessandro Saviolo, Jonathan Frey, Abhishek Rathod +2
Model-based control requires an accurate model of the system dynamics for precisely and safely controlling the robot in complex and dynamic environments. Moreover, in the presence…
Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking
Alessandro Saviolo, Guanrui Li, Giuseppe Loianno
Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. The model needs to capture the system behavior in multiple flight r…
AutoTune: Controller Tuning for High-Speed Flight
Antonio Loquercio, Alessandro Saviolo, Davide Scaramuzza
Due to noisy actuation and external disturbances, tuning controllers for high-speed flight is very challenging. In this paper, we ask the following questions: How sensitive are con…
Learning to Segment Human Body Parts with Synthetically Trained Deep Convolutional Networks
Alessandro Saviolo, Matteo Bonotto, Daniele Evangelista +4
This paper presents a new framework for human body part segmentation based on Deep Convolutional Neural Networks trained using only synthetic data. The proposed approach achieves c…