Antonello Ceravola
Papers
6
Total Citations
133
H-Index
4
About
Antonello Ceravola is a leading researcher at the intersection of artificial intelligence and robotics, specializing in human-robot interaction (HRI), multi-agent systems, and the application of Large Language Models (LLMs) to autonomous systems. His most impactful work, "LaMI: Large Language Models for Multi-Modal Human-Robot Interaction" (2024), has already garnered 64 citations for pioneering an LLM-based framework that simplifies intent estimation and behavior generation—overcoming the resource-intensive limitations of traditional HRI designs. Ceravola further advances autonomous capabilities with "CoPAL: Corrective Planning of Robot Actions with Large Language Models" (2024, 25 citations), which addresses the challenge of open-world task execution through LLM-driven corrective planning. His foundational contributions include developing integrated research environments for real-time distributed intelligent systems (2006, 19 citations), establishing infrastructure that supports complex, multi-agent coordination. Ceravola’s work on designing interaction for multi-agent systems in office environments (2020–2021) demonstrates his commitment to practical, user-centered AI, enabling seamless collaboration between mobile robots, smart devices, and human users. With a career spanning foundational infrastructure to cutting-edge LLM applications, Ceravola’s research is shaping the future of responsive, autonomous intelligent systems.
Research Focus
Key Achievements
Top Papers
- 1LaMI: Large Language Models for Multi-Modal Human-Robot Interaction64 citations · 2024
- 2CoPAL: Corrective Planning of Robot Actions with Large Language Models25 citations · 2024
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- 5Designing Interaction for Multi-agent System in an Office Environment4 citations · 2020
- 6