Welcome!
I am currently pursuing my Ph.D. at the Agile Robotics and Perception Lab (ARPL) at New York University, under the esteemed guidance of Prof. Giuseppe Loianno. I hold a Bachelor's and Master's degree in Computer Engineering from the University of Padova. During my Master's program, I had the enriching experience of spending a semester at National Chao Tung University in Taiwan. Furthermore, I was honored to receive a scholarship through the Swiss European Mobility Programme, which allowed me to engage in a six-month research project at the Robotics and Perception Group in Zurich. Following the completion of my Master's degree, I spent nine months as a full-time software engineer at Flexsight, a cutting-edge start-up specializing in artificial vision and autonomous perception solutions.
Research
Dynamics Learning
Model-based control needs an accurate system dynamics model to safely and precisely control robots in complex environments. This model should adapt to changing conditions. Our research introduces a self-supervised learning method that actively models nonlinear robotic systems' dynamics. We combine offline learning from past data and online learning from current interactions, making the process highly sample-efficient and adaptive. We also design an uncertainty-aware model predictive controller that considers data uncertainty to choose optimal control actions, improving both performance and learning efficiency.
Visual Tracking
Visual control allows quadrotors to navigate using real-time sensory data, but challenges like generalization, reliability, and real-time response remain. Our research addresses these issues with a new perception framework using foundation models for universal object detection and tracking. This framework, combined with a multi-layered tracker, ensures continuous target visibility despite motion blur, light changes, and occlusions. We also introduce a model-free visual controller for resilient tracking. Our system works efficiently with limited hardware, using only an onboard camera and an inertial measurement unit.
Perpetual Autonomy
Battery endurance is a major challenge for long-term autonomy and long-range aerial robot operations. Our solution, AutoCharge, addresses this with an autonomous charging system for quadrotors. AutoCharge combines a portable ground station with a lightweight, flexible charging tether for universal, efficient, and robust charging. We designed circular magnetic connectors for precise, orientation-agnostic connections between the ground station and tether. An electromagnet on the ground station increases tolerance to localization and control errors during docking, ensuring smooth undocking after charging.
Latest News
- May, 2024. Will present in person at ICRA Agile Robotics workshop ✈️
- May, 2024. Will present in person at ICRA Aerial Robotics workshop ✈️
- May, 2024. Will present in person at ICRA 2024 conference in Yokohama ✈️
- Jan, 2024. Paper article accepted at ICRA 2024 🦾
- Nov, 2023. Journal article accepted at Transactions on Robotics 2023 🦾
- Oct, 2023. Paper article featured on IEEE Spectrum 📺
- Oct, 2023. Paper article accepted at ICAR 2023 🦾
- Jun, 2023. AutoCharge featured on IEEE Spectrum 📺
- May, 2023. Will present in person at ICRA Energy Efficient Aerial Robotics workshop ✈️
- May, 2023. Will present in person at ICRA 2023 conference in London ✈️
- May, 2023. Honored to have been awarded the Dr. Li Annual ECE Publication Award 🏆
- Mar, 2023. Journal article accepted at Annual Reviews in Control 2023 🦾
- Jan, 2023. AutoCharge and GaPT accepted at ICRA 2023 conference 🦾🦾
- Oct, 2022. Will present virtually at IROS 2022 conference 📺
- Jun, 2022. PI-TCN accepted at RAL+IROS 2022 🦾
- May, 2022. Will present in person at ICRA 2022 conference in Philadelphia ✈️
- Jan, 2022. AutoTune accepted at RAL+ICRA 2022 🦾
- Aug, 2021. Joined Agile Robotics and Perception Lab 👨🎓