Francesco De Lellis
Papers
2
Total Citations
4
H-Index
2
About
Francesco De Lellis is a leading researcher at the intersection of robotics, cognitive architectures, and human-machine interaction. His work focuses on developing data-driven and learning-based frameworks to enhance coordination and communication within human-avatar and human-robot groups. A key contribution is his pioneering use of reinforcement learning to design advanced cognitive architectures that improve group dynamics in mixed human-agent teams, addressing the critical challenge of seamless collaboration in physical and virtual environments. In parallel, De Lellis has introduced novel data-driven control strategies that encode specific information—such as emotional states—directly into the kinematics of robots and avatars. This approach leverages human-recorded examples to generate information-rich movements, enabling more intuitive and expressive interactions. Though his most cited works are recent (2024), they are already garnering attention, with papers accumulating 2 citations each. De Lellis’s work is poised to significantly impact the future of teleoperation, social robotics, and immersive virtual reality, offering foundational tools for building more responsive and emotionally intelligent autonomous agents.
Research Focus
Key Achievements
Top Papers
- 1
- 2