Silvia Cruciani
Papers
12
Total Citations
196
H-Index
7
About
Silvia Cruciani is a leading researcher in robotic manipulation, whose work is fundamentally reshaping how robots handle and reorient objects within their grasp. Her primary research focuses on dexterous in-hand manipulation, developing innovative frameworks for enabling robots to pivot, regrasp, and reposition objects using parallel grippers without relying on complex, high-fidelity hardware. Cruciani’s most significant contribution is the introduction of the **Dexterous Manipulation Graph (DMG)** , a powerful tool for planning sequences of manipulation primitives to achieve desired object poses. Her highly cited benchmark paper (48 citations) provides a standardized evaluation for in-hand manipulation systems, while her work on reinforcement learning for pivoting tasks (36 citations) demonstrates how robust policies can be learned without prior dynamic models. With over 190 total citations, Cruciani has also pioneered methods for integrating visual feedback and dual-arm coordination into manipulation planning. Her notable achievements include developing open-loop pivoting strategies that leverage arm motion and controlled friction, making advanced manipulation accessible without specialized grippers. For students and researchers, Cruciani’s work offers a practical, systematic approach to one of robotics’ most challenging problems: giving machines the same manipulative dexterity as human hands.
Research Focus
Key Achievements
Top Papers
- 1Benchmarking In-Hand Manipulation48 citations · 2020
- 2Reinforcement Learning for Pivoting Task36 citations · 2017
- 3Dexterous Manipulation Graphs32 citations · 2018
- 4In-hand manipulation using three-stages open loop pivoting23 citations · 2017
- 5From Visual Understanding to Complex Object Manipulation16 citations · 2018
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- 9Integrating Path Planning and Pivoting5 citations · 2018
- 10Dual-Arm In-Hand Manipulation Using Visual Feedback5 citations · 2019