Simone Silenzi
Papers
1
Total Citations
5
H-Index
1
About
Simone Silenzi is a robotics researcher whose work bridges the gap between high-level language understanding and low-level robotic manipulation. His primary research areas include semantic grasping, human-robot interaction, and the application of large language models (LLMs) to physical tasks. Silenzi’s most notable contribution is the development of Lan-grasp, a system that leverages foundation models to give robots a semantic understanding of object geometry. This allows a robot to identify not just *how* to grasp an object, but *where*—determining the appropriate part to hold, which areas to avoid, and the natural orientation for placement. This work, published in 2023, has already garnered 5 citations, signaling its relevance to the growing field of LLM-driven robotics. By moving beyond simple pick-and-place operations, Silenzi’s research enables more intuitive and context-aware robot behavior, a critical step toward machines that can operate safely and effectively in human environments. His work is particularly valuable for students and researchers interested in the intersection of natural language processing and embodied AI.
Research Focus
Key Achievements
Top Papers
- 1