🤖 Robotics Engineer working on autonomous robotic systems, with a strong focus on control, navigation, and embodied intelligence.

🚀 I design and deploy robots that must operate in real environments, where perception is incomplete, contact is unavoidable, and robustness matters more than clean demos.

🎓 Research Fellow at Istituto Italiano di Tecnologia (IIT) 🤖 Working on the R1 humanoid robot, contributing to autonomy pipelines that span perception, planning, and control, with experimental validation on physical hardware.

My work involves system-level integration and real-world testing, rather than isolated algorithmic components.

My research interests sit at the intersection of Vision-Language-Action models, tactile sensing, and adaptive control for contact-rich manipulation.

• Adaptive robotic manipulation in unstructured environments Learning how robots can remain reliable when contact dynamics, friction, and object poses deviate from demonstrations.

• Vision-Language-Action models beyond open-loop execution Studying how foundation policies such as diffusion- and flow-based VLAs can be augmented, rather than retrained, to improve robustness during physical interaction.

• Tactile-guided residual reinforcement learning Developing small, bounded RL correction modules that leverage tactile and proprioceptive feedback to compensate for misalignment, slip, and jamming during contact, while keeping high-level VLA policies unchanged.

• Embodied and closed-loop learning Focusing on learning mechanisms that exploit physical interaction, instead of relying solely on offline imitation, to achieve safer and more transferable robot behavior.

• Neuromorphic and efficient sensing-action pipelines Exploring spiking and event-based approaches for low-latency, energy-efficient feedback in active exploration and manipulation.

This direction is inspired by my PhD proposal on Adaptive Vision-Language-Action Models through Tactile-Guided Residual Reinforcement Learning for Contact-Rich Manipulation.

• 🛰️ Autonomous Navigation ROS2, SLAM, metric and topological mapping, global and local planning, Nav2, real-world deployment

• 🧭 Motion Planning and Control State feedback control, LQR, MPC, task-space and impedance-style control

• 🧠 Robot Learning Reinforcement learning, learning-based perception, policy adaptation and residual learning

• 🤝 Multi-agent and multi-robot systems Exploration, task allocation, coordination, decentralized strategies

• 🧪 Simulation and sim-to-real validation Gazebo Ignition, Robotarium, ManiSkill, Isaac Gym

• Languages: C++, Python, MATLAB, C, Java, XML, CMake
• Frameworks & Tools: ROS2, YARP, Git, Simulink, Stateflow, RViz, Nav2
• Simulation: Gazebo, Ignition, Robotarium, ManiSkill, Isaac Gym
• Control: LQR, MPC, FOC, IOC, CLIK, PLC

• 🧭 T-BOT – Topological Navigation for Multi-Robot Exploration Autonomous fleet management with ROS2 and TurtleBot4, based on Voronoi partitioning, Chinese Postman Problem, and dynamic task allocation.

• 🦾 Cartesian Impedance Control on Franka Emika Panda Task-space control with collision-aware trajectory tracking using ROS2 and MoveIt2, validated in Gazebo Ignition.

• 🚧 Indoor Autonomous Navigation on TurtleBot4 Extended Nav2 pipeline with vision-based sign recognition and real-world experiments.

• 🔍 Mechanical and Electromechanical Control Projects Linear and nonlinear controllers for underactuated systems and DC motor regulation.

• 🔥 Fire and Smoke Detection 3D CNN-based perception system for hazard detection in real environments.

🎓 M.Sc. in Automation and Control Systems Engineering – Università degli Studi di Salerno Thesis: T-BOT: The Navigation Robot for Optimized Multi-Agent Exploration

🎓 B.Sc. in Computer Engineering – Università degli Studi di Salerno Thesis: Monitor4U