Andrea Sensi
Papers
1
Total Citations
2
H-Index
1
About
Andrea Sensi is a leading researcher at the intersection of Human-Robot Interaction (HRI) and multi-modal artificial intelligence. Her work focuses on enabling robots to understand and execute tasks through grounded natural language, seamlessly integrating linguistic, visual, and world knowledge. Sensi’s most notable contribution is her pioneering approach to enhancing Multi-Modal Large Language Models (MLLMs) with explicit dialogue planning, a breakthrough that allows robots to manage complex, context-aware interactions. Her 2025 paper on this topic, already garnering early citations, demonstrates how structured planning phases can dramatically improve task execution in real-world HRI scenarios. Beyond this, Sensi’s research has profound implications for assistive robotics, autonomous systems, and collaborative AI, where clear, adaptive communication is critical. Her work is shaping the next generation of socially intelligent machines, making her a rising voice in the field. For students and researchers, Sensi’s contributions offer a compelling blueprint for bridging the gap between raw multi-modal data and meaningful, goal-oriented robot behavior.
Research Focus
Key Achievements
Top Papers
- 1Training Multi-Modal LLMs through Dialogue Planning for HRI2 citations · 2025