Luca Baronti
Papers
3
Total Citations
39
H-Index
3
About
Luca Baronti is a researcher at the intersection of robotics, manipulation, and intelligent automation. His primary research areas include robotic grasping, post-grasp manipulation dynamics, and the application of deep learning and optimization algorithms to industrial tasks. Baronti’s most cited work, “Analysis of the inertia and dynamics of grasped objects, for choosing optimal grasps to enable torque-efficient post-grasp manipulations” (2016, 24 citations), makes a significant contribution by addressing a critical gap in robotic manipulation: selecting grasps that not only secure an object but also minimize torque and energy consumption during subsequent movements. This work integrates knowledge of an object’s mass distribution and inertia tensor with the robot’s dynamic model, enabling more efficient and dexterous manipulation. In his more recent research, Baronti has advanced the automation of industrial disassembly by employing the PointNet deep neural network for automatic identification of mechanical parts (2022, 12 citations), and has explored shape recognition using the Bees Algorithm (2022, 3 citations). These contributions demonstrate a clear trajectory toward making robotic systems more autonomous, efficient, and capable in complex, real-world manufacturing and recycling environments.
Research Focus
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Top Papers
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