Francesco Riccio

About

Francesco Riccio is a robotics and artificial intelligence researcher whose work spans multi-robot coordination, human-robot interaction, and reinforcement learning. His research addresses some of the most challenging problems in modern robotics: enabling teams of autonomous agents to cooperate intelligently in dynamic, uncertain environments while remaining responsive and accessible to human collaborators. Riccio's most influential contribution, "Game Strategies for Physical Robot Soccer Players: A Survey" (2021, 34 citations), synthesizes decades of research on team-based robotic decision-making, establishing a foundational reference for the field. His sustained interest in multi-robot systems is further reflected in earlier work on context-aware task assignment and world modeling, demonstrating how robots can adapt coordination strategies in real time. Alongside this, Riccio has made meaningful strides in human-robot collaboration, exploring how users perceive and engage with robots through studies on collaboration attitude and symbiotic autonomy. His work on semantic mapping, object-level scene understanding, and deep reinforcement learning — including the development of Deep Optimistic Planning — highlights a researcher equally comfortable bridging theoretical AI methods with practical robotic deployment. With over 70 cumulative citations across a diverse and cohesive body of work, Riccio represents a versatile voice in contemporary robotics research, consistently pushing toward systems that are smarter, more cooperative, and more human-aware.

Research Focus

Key Achievements

Top Papers

1. 1
   Game Strategies for Physical Robot Soccer Players: A Survey

   34 citations · 2021
2. 2
   Who is Willing to Help Robots? A User Study on Collaboration Attitude

   14 citations · 2019
3. 3
   Context in Robotics and Information Fusion

   12 citations · 2016
4. 4
   Multi-robot search for a moving target: Integrating world modeling, task assignment and context

   10 citations · 2016
5. 5
   Enabling Symbiotic Autonomy in Short-Term Interactions: A User Study

   7 citations · 2016
6. 6
   S-AvE: Semantic Active Vision Exploration and Mapping of Indoor Environments for Mobile Robots

   6 citations · 2021
7. 7
   Learning human-robot handovers through π-STAM: Policy improvement with spatio-temporal affordance maps

   5 citations · 2016
8. 8
   Context-Based Coordination for a Multi-Robot Soccer Team

   4 citations · 2015
9. 9
   DOP: Deep Optimistic Planning with Approximate Value Function Evaluation

   4 citations · 2018
10. 10
    Cooperative Multi-agent Deep Reinforcement Learning in a 2 Versus 2 Free-Kick Task

    3 citations · 2019

Key Collaborators

Contact & Links