Davide Nicolis
Papers
9
Total Citations
259
H-Index
7
About
Davide Nicolis is a leading researcher in the field of robotics, with a focus on control systems for human-robot interaction, teleoperation, and redundant manipulators. His work bridges robust control theory and practical applications, particularly through the integration of sliding mode control (SMC) and model predictive control (MPC) for impedance and force control. His most cited paper, "Operational Space Model Predictive Sliding Mode Control for Redundant Manipulators" (99 citations), introduces a novel robust controller that combines the disturbance rejection of SMC with the predictive capabilities of MPC, significantly advancing the performance of redundant robotic arms. Nicolis has also made key contributions to shared autonomy teleoperation, as seen in his work on occlusion-free visual servoing for dual-arm robots (73 citations), which enhances operator experience and task reliability. His research on human intention estimation using neural networks (28 citations) further demonstrates his commitment to intuitive and safe human-robot collaboration. With a portfolio spanning constraint-based control, sensorless force control, and virtual fixtures, Nicolis has established himself as a pivotal figure in developing robust, user-centered robotic systems, with his work cited over 250 times and influencing both academic research and industrial robotics.
Research Focus
Key Achievements
Top Papers
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- 5Implicit Robot Force Control Based on Set Invariance12 citations · 2017
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