Silvia Cascianelli
University of Perugia, University of Modena and Reggio Emilia
Papers
8
Total Citations
165
H-Index
6
About
Silvia Cascianelli is a researcher whose work spans computer vision, autonomous robotics, and human-robot interaction, with a particular focus on making robotic systems more intelligent, communicative, and spatially aware. Her most cited contribution, "Robust visual semi-semantic loop closure detection" (2017, 48 citations), addresses a fundamental challenge in robot navigation by combining covisibility graphs with deep CNN features to improve localization reliability. Her work on agricultural robotics has also gained significant traction, with her quasi-unsupervised approach to fruit counting across domains earning 38 citations and demonstrating practical value for precision agriculture. Cascianelli has made notable strides in natural language interfaces for service robots, developing Full-GRU video description systems that enable more intuitive human-robot communication, accumulating 35 citations. Her more recent investigations into deep reinforcement learning for indoor exploration and embodied agents capable of scene description reflect a forward-looking research trajectory that bridges perception, language, and autonomous decision-making. Across her portfolio, Cascianelli consistently bridges theoretical machine learning advances with real-world robotic applications, establishing herself as a versatile and impactful contributor to intelligent systems research.
Research Focus
Key Achievements
Top Papers
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- 4Focus on Impact: Indoor Exploration With Intrinsic Motivation19 citations · 2022
- 5
- 6The Role of the Input in Natural Language Video Description6 citations · 2019
- 7
- 8Embodied Agents for Efficient Exploration and Smart Scene Description5 citations · 2023