Papers
32
Total Citations
682
H-Index
15
About
Dario Piga is a prominent researcher specializing in human-robot collaboration, adaptive robot control, and machine learning for robotic systems. His work sits at the intersection of control theory, reinforcement learning, and intelligent automation, with a particular focus on enabling robots to operate effectively in uncertain, dynamic environments characteristic of Industry 4.0 settings. Among his most influential contributions is his development of adaptive impedance control frameworks, including Q-Learning-based model predictive variable impedance control and robust state-dependent Riccati equation approaches, which allow robots to intelligently modulate their interaction forces during collaboration with humans. His research on sensorless environment stiffness estimation has been especially impactful, enabling robots to adapt to partially unknown environments without requiring dedicated force sensors—a practically significant achievement for industrial deployment. Piga has also made substantial contributions to reinforcement learning for robotic manipulation and grasping, exploring both on-policy and off-policy algorithms to bridge the gap between simulation and real-world performance. His work on Bayesian Optimization for uncertainty adaptation further demonstrates his commitment to data-efficient learning methods. With numerous papers accumulating between 23 and 77 citations each, his cumulative research impact reflects a growing influence across robotics, rehabilitation engineering, and intelligent manufacturing communities worldwide.
Research Focus
Key Achievements
Top Papers
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- 3Robot control parameters auto-tuning in trajectory tracking applications61 citations · 2020
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- 10Sensorless Optimal Switching Impact/Force Controller23 citations · 2021