Emanuele Musumeci

Email ing.emanuele.musumeci@gmail.com

Phone +39 3206136429

Let me introduce myself...

I'm specialized in Engineering in Artificial Intelligence and Robotics, with experience in Planning and Logical Reasoning, control of Autonomous Multi-Agent systems and Human-Robot Interaction routines. 

I have a Bachelor's Degree from Sapienza University of Rome in Computer Engineering and Automation, from which I graduated with honors, with the thesis "Triangulation of a light source for an autonomous mobile robot".

I am currently a student in the Master's degree of Artificial Intelligence and Robotics and a researcher. My research activity lead to the publication of two academic papers about Dynamic Behavior Generation of Robot Behaviors with Temporal Constraints, at the RoboCup Symposium (2022 and 2023), and then of a third paper about Multi agent LLM-assisted document generation at the HCI Interational 2024 Conference.

As a member of the SPQR RoboCup Team at Sapienza University, I took part in the RoboCup SPL competition in 2021 and 2022, desining the software for our team of autonomous humanoid NAO robotic soccer players. Learn more about RoboCup SPL here! 


Fast facts


My main skill

Working on complex systems, with multiple programming languages and frameworks involved, requiring a broad computer engineering  and robotics knowledge.


"Adaptive Team Behavior Planning using Human Coach Commands", Musumeci E., Suriani V., Antonioni E., Nardi D., Bloisi D.D. (2023). RoboCup 2022. Lecture Notes in Computer Science(), vol 13561. Springer, Cham. (Google Scholar).

"Play Everywhere: A Temporal Logic based Game Environment Independent Approach for Playing Soccer with Robots", Suriani V., Musumeci E., Nardi D., Bloisi D.D., Lecture notes in computer science-RoboCup 2023: Robot World Cup XXVI (Google Scholar)

"LLM Based Multi-Agent Generation of Semi-structured Documents from Semantic Templates in the Public Administration Domain", Musumeci E., Brienza M., Suriani V., Nardi D., Bloisi D. D., [Presented at HCI International 2024]. (Google Scholar)


Task-based Human-Robot interaction routine in a Robot Soccer setting

This video shows the demo of a human-robot interaction routine, based on the autonomous completion of tasks issued through a GUI on an external device (a laptop, tablet or smartphone), chosen among a predefined library of soccer-related tasks. The robotic agent is a child-sized humanoid NAO robot, by Aldebaran, now acquired by SoftBanks Robotics.

The software being used on the robot is a fork of the framework used by the SPQRTeam in RoboCup SPL competitions, in turn based on the B-Human RoboCup SPL framework. 

Languages involved:

RoboCup Coach

Project published with the paper "Adaptive Team Behavior Planning using Human Coach Commands"

The idea behind this project is to make a first step towards real-time coaching of robots in RoboCup Soccer SPL, by using online planning in a PDDL domain using temporal goals and constraints. The presented architecture allows a human in the loop to speak temporal goals over PDDL predicates, influencing robot behaviors in real-time.

Languages involved:


A browser game, developed as a final project for an Interactive Graphics exam, featuring a series of “mini-games” (easy levels with a specific objective), designed modularly to allow an easy addition of new levels. It is implemented using the Babylon.js framework and Cannon.js as a physics engine. After a first round where all mini-games are played in sequence, mini-games will be chosen randomly and randomized (each mini-game provides its own randomization features). The player is implemented as a hierarchical model representing a “drawing mannequin” with animations for walking (forward, left, right, back), jumping, falling and being idle. 

Languages involved:

Unsupervised feature selection for clustering in high-dimensional spaces

This library provides a vanilla implementation of a genetic algorithm. The library is then used to attempt a novel approach to perform unsupervised feature selection for clustering in high-dimensional spaces. Probably not publishable or peer-reviewable but at least I had fun implementing it.

UrbanSound8K sound classification using a Convolutional Neural Network

Classifier based on a convolutional neural network, to solve the environmental sound classification task. The CNN is trained using audio data taken from the UrbanSound8K dataset, with the addition of various feature extraction techniques:

TRPO algorithm in Tensorflow 2

Small homemade modular framework created as a University project, to implement and test Reinforcement Learning algorithms, with a working implementation of TRPO.

Pass Planner for RoboCup SPL

As a member of SPQR Team, I contributed to the development of the "Pass Planner", an Artificial Intelligence System based on Automatic Planning algorithms (Dijkstra's algorithm for the search of shortest-paths and A* algorithm for heuristic search), to compute the best pass sequence to implement, during RoboCup SPL matches, effective collaborative strategies, culminating in a successful action, to score in the opponent goal.

Play Everywhere

As an extension to previous work, we published "Play Everywhere: A Temporal Logic based Game Environment Independent Approach for Playing Soccer with Robots"

Here, we proposed a temporal logic-based approach that allows robots’ behaviors and goals to adapt to the semantics of the environment.   

The soccer setting is modeled hierarchically and the robot selects the level of operation based on the perceived semantic characteristics of the environment, thus dynamically modifying the set of rules and goals to apply in an unstructured environment.

LLM Multi-Agent Generation of Documents from Semantic Templates 

The recent introduction of Large Language Models has enabled the creation of customized text output satisfying user requests. In this work, we propose a novel approach that combines the LLMs with prompt engineering and multi-agent systems for generating new documents compliant with a desired structure. The main contribution of this work concerns replacing the commonly used manual prompting with a task description generated by semantic retrieval from an LLM. The potential of this approach is demonstrated through a series of experiments and case studies, showcasing its effectiveness in real-world Public Administration scenarios.


RoboCup 2021 Worldwide

Due to the COVID-19 pandemic, the annual RoboCup event was held remotely (link to the official website). This saw our team of robots facing 4 different challenges:

Maker Faire 2021 Rome Edition

Maker Faire is an event that hosts inventors, makers and researchers from all over the world, where each participant can share his ideas or showcase his inventions.

I took part in this event as a member of SPQR Team. We invited the HULKs Team (a german RoboCup SPL team) as adversaries for a small RoboCup tournament. 

We also took the chance to showcase the reality of RoboCup SPL and the skills of our robots.

RoboCup 2022 Bangkok

RoboCup is a the main event in Robotics and Artificial Intelligence, taking place every year in different locations all over the world.

It features expositions, pitches and seminars but its main focus is posed on robotics tournaments.

I took part in this event as a member of SPQR Team from RoCoCo Lab of Sapienza University of Rome

After the end of RoboCup 2022 I presented the paper "Adaptive Team Behavior Planning using Human Coach Commands" (link) at the RoboCup Symposium 2022.

Maker Faire 2022 - The European Edition

I took part in the 2022 edition of Maker Faire as a member of SPQR Team. We invited the Nao Devils team (a german RoboCup SPL team) as adversaries.